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Oliver Bruce Linton

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Dong, C. & Gao, J. & Linton, O. & Peng, B., 2020. "On Time Trend of COVID-19: A Panel Data Study," Cambridge Working Papers in Economics 2065, Faculty of Economics, University of Cambridge.

    Mentioned in:

    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Modelling > Statistical Modelling

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Lena Boneva & Oliver Linton & Michael Vogt, 2016. "The Effect of Fragmentation in Trading on Market Quality in the UK Equity Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 192-213, January.

    Mentioned in:

    1. The Effect of Fragmentation in Trading on Market Quality in the UK Equity Market (Journal of Applied Econometrics 2016) in ReplicationWiki ()
  2. Keith Vorkink & Douglas J. Hodgson & Oliver Linton, 2002. "Testing the capital asset pricing model efficiently under elliptical symmetry: a semiparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 617-639.

    Mentioned in:

    1. Testing the capital asset pricing model efficiently under elliptical symmetry: a semiparametric approach (Journal of Applied Econometrics 2002) in ReplicationWiki ()

Working papers

  1. Hafner, Christian & Linton, Oliver & Wang, Linqi, 2022. "Dynamic Autoregressive Liquidity (DArLiQ)," LIDAM Discussion Papers LFIN 2022002, Université catholique de Louvain, Louvain Finance (LFIN).

    Cited by:

    1. Bachmair, K., 2023. "The Effects of the LIBOR Scandal on Volatility and Liquidity in LIBOR Futures Markets," Cambridge Working Papers in Economics 2303, Faculty of Economics, University of Cambridge.

  2. Liang Jiang & Oliver B. Linton & Haihan Tang & Yichong Zhang, 2022. "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance," Papers 2201.13004, arXiv.org, revised Jun 2023.

    Cited by:

    1. Liang Jiang & Liyao Li & Ke Miao & Yichong Zhang, 2023. "Adjustment with Many Regressors Under Covariate-Adaptive Randomizations," Papers 2304.08184, arXiv.org, revised Feb 2024.

  3. Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & David Abad-Dí­az & Menachem Abudy & Tobi, 2021. "Non-Standard Errors," Working Papers 2021-31, Faculty of Economics and Statistics, Universität Innsbruck.

    Cited by:

    1. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    2. Dreber, Anna & Johannesson, Magnus, 2023. "A framework for evaluating reproducibility and replicability in economics," Ruhr Economic Papers 1055, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. Fišar, Miloš & Greiner, Ben & Huber, Christoph & Katok, Elena & Ozkes, Ali & Collaboration, Management Science Reproducibility, 2023. "Reproducibility in Management Science," OSF Preprints mydzv, Center for Open Science.
    4. Christoph Huber & Christian König-Kersting, 2022. "Experimenting with Financial Professionals," Working Papers 2022-07, Faculty of Economics and Statistics, Universität Innsbruck.
    5. Christophe Pérignon & Olivier Akmansoy & Christophe Hurlin & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johanneson & Michael Kirchler & Albert Menkveld & Michael Razen & Utz Weitzel, 2022. "Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance," Working Papers hal-03810013, HAL.
    6. Breznau, Nate & Rinke, Eike Mark & Wuttke, Alexander & Nguyen, Hung H. V. & Adem, Muna & Adriaans, Jule & Alvarez-Benjumea, Amalia & Andersen, Henrik K. & Auer, Daniel & Azevedo, Flavio & Bahnsen, Oke, 2022. "Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 119(44), pages 1-8.
    7. Müller, Isabella & Noth, Felix & Tonzer, Lena, 2022. "A note on the use of syndicated loan data," IWH Discussion Papers 17/2022, Halle Institute for Economic Research (IWH).
    8. Stephen A. Gorman & Frank J. Fabozzi, 2023. "Alternative risk premium: specification noise," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 459-473, October.

  4. Li, M. Z. & Linton, O., 2021. "Robust Estimation of Integrated and Spot Volatility," Cambridge Working Papers in Economics 2115, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Minseog Oh & Donggyu Kim, 2021. "Effect of the U.S.--China Trade War on Stock Markets: A Financial Contagion Perspective," Papers 2111.09655, arXiv.org.

  5. Linton, O. & Tang, H., 2020. "Estimation of the Kronecker Covariance Model by Quadratic Form," Cambridge Working Papers in Economics 2050, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Bhimasankaram Pochiraju & Sridhar Seshadri & Dimitrios D. Thomakos & Konstantinos Nikolopoulos, 2020. "Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization," Stats, MDPI, vol. 3(3), pages 1-18, July.
    2. Yuefeng Han & Rong Chen & Cun-Hui Zhang, 2020. "Rank Determination in Tensor Factor Model," Papers 2011.07131, arXiv.org, revised May 2022.

  6. Anderson, G. & Linton, O. & Pittau, M G. & Whang, Y-J. & Zelli, R., 2020. "On Unit Free Assessment of The Extent of Multilateral Distributional Variation," Cambridge Working Papers in Economics 20123, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Gordon Anderson & Maria Grazia Pittau & Roberto Zelli, 2020. "Measuring the progress of equality of educational opportunity in absence of cardinal comparability," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 155-174, August.
    2. Anderson, Gordon & Fu, Rui & Leo, Teng Wah, 2022. "Health, loneliness and the ageing process in the absence of cardinal measure: Rendering intangibles tangible," The Journal of the Economics of Ageing, Elsevier, vol. 22(C).

  7. Chaohua Dong & Jiti Gao & Oliver Linton & Bin peng, 2020. "On Time Trend of COVID-19: A Panel Data Study," Monash Econometrics and Business Statistics Working Papers 22/20, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Giovanni Angelini & Giuseppe Cavaliere & Enzo D'Innocenzo & Luca De Angelis, 2022. "Time-Varying Poisson Autoregression," Papers 2207.11003, arXiv.org.

  8. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Daniele Valenti & Andrea Bastianin & Matteo Manera, 2022. "A weekly structural VAR model of the US crude oil market," Working Papers 2022.11, Fondazione Eni Enrico Mattei.
    2. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Miffre, Joelle, 2021. "The risk premia of energy futures," Energy Economics, Elsevier, vol. 102(C).
    3. Johannes Rude Jensen & Mohsen Pourpouneh & Kurt Nielsen & Omri Ross, 2021. "The Homogenous Properties of Automated Market Makers," Papers 2105.02782, arXiv.org.
    4. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2021. "How does the financial market update beliefs about the real economy? Evidence from the oil market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 938-961, November.
    5. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.

  9. Bonsoo Koo & Davide La Vecchia & Oliver Linton, 2020. "Estimation of a Nonparametric Model for Bond Prices from Cross-Section and Time Series Information," Monash Econometrics and Business Statistics Working Papers 4/20, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Frazier, David T. & Koo, Bonsoo, 2021. "Indirect inference for locally stationary models," Journal of Econometrics, Elsevier, vol. 223(1), pages 1-27.
    2. David T. Frazier & Bonsoo Koo, 2020. "Indirect Inference for Locally Stationary Models," Monash Econometrics and Business Statistics Working Papers 30/20, Monash University, Department of Econometrics and Business Statistics.

  10. Li, S. & Linton, O., 2020. "When will the Covid-19 pandemic peak?," Cambridge Working Papers in Economics 2025, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Fernández-Villaverde, Jesús & Jones, Chad, 2020. "Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States, and Cities," CEPR Discussion Papers 14711, C.E.P.R. Discussion Papers.
    2. Andrew Atkeson & Karen Kopecky & Tao Zha, 2020. "Estimating and Forecasting Disease Scenarios for COVID-19 with an SIR Model," NBER Working Papers 27335, National Bureau of Economic Research, Inc.
    3. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    4. Bårdsen, Gunnar & Nymoen, Ragnar, 2023. "Dynamic time series modelling and forecasting of COVID-19 in Norway," Memorandum 3/2023, Oslo University, Department of Economics.
    5. Shaw, Norman & Eschenbrenner, Brenda & Baier, Daniel, 2022. "Online shopping continuance after COVID-19: A comparison of Canada, Germany and the United States," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    6. Chaohua Dong & Jiti Gao & Oliver Linton & Bin Peng, 2020. "On the Time Trend of COVID-19: A Panel Data Study," Papers 2006.11060, arXiv.org, revised Jun 2020.
    7. Ricardo Martínez & Juan D Moreno Ternero, 2021. "Pandemic performance," ThE Papers 21/09, Department of Economic Theory and Economic History of the University of Granada..
    8. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    9. Ricardo Martínez & Juan D Moreno Ternero, 2022. "An axiomatic approach towards pandemic performance indicators," ThE Papers 22/12, Department of Economic Theory and Economic History of the University of Granada..
    10. Andrew Atkeson, 2020. "On Using SIR Models to Model Disease Scenarios for COVID-19," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 41(01), pages 1-35, June.
    11. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2020. "Voluntary and Mandatory Social Distancing: Evidence on COVID-19 Exposure Rates from Chinese Provinces and Selected Countries," NBER Working Papers 27039, National Bureau of Economic Research, Inc.
    12. Sokbae (Simon) Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2020. "Sparse HP filter: Finding kinks in the COVID-19 contact rate," CeMMAP working papers CWP32/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020. "Local mortality estimates during the COVID-19 pandemic in Italy," Working Papers 14/20, Sapienza University of Rome, DISS.
    14. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    15. Monika Małgorzata Wojcieszak-Zbierska & Anna Jęczmyk & Jan Zawadka & Jarosław Uglis, 2020. "Agritourism in the Era of the Coronavirus (COVID-19): A Rapid Assessment from Poland," Agriculture, MDPI, vol. 10(9), pages 1-19, September.
    16. Julliard, Christian & Shi, Ran & Yuan, Kathy, 2023. "The spread of COVID-19 in London: Network effects and optimal lockdowns," Journal of Econometrics, Elsevier, vol. 235(2), pages 2125-2154.
    17. David Meintrup & Martina Nowak-Machen & Stefan Borgmann, 2021. "Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries," IJERPH, MDPI, vol. 18(12), pages 1-17, June.
    18. Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, vol. 220(1), pages 2-22.
    19. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    20. Christian M. Hafner, 2020. "The Spread of the Covid-19 Pandemic in Time and Space," IJERPH, MDPI, vol. 17(11), pages 1-13, May.
    21. Ba Chu & Shafiullah Qureshi, 2020. "Predicting the COVID-19 Pandemic in Canada and the US," Carleton Economic Papers 20-05, Carleton University, Department of Economics, revised 30 Jul 2020.
    22. Zubarev, Andrei & Kirillova, Maria, 2022. "Modeling COVID-19 spread in the Russian Federation using global VAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 117-138.
    23. Belhadi, Amine & Kamble, Sachin & Jabbour, Charbel Jose Chiappetta & Gunasekaran, Angappa & Ndubisi, Nelson Oly & Venkatesh, Mani, 2021. "Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    24. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2023. "How to go viral: A COVID-19 model with endogenously time-varying parameters," Journal of Econometrics, Elsevier, vol. 232(1), pages 70-86.
    25. Aman Ullah & Tao Wang & Weixin Yao, 2022. "Nonlinear modal regression for dependent data with application for predicting COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1424-1453, July.
    26. Gunnar BÃ¥rdsen & Ragnar Nymoen, 2023. "Dynamic time series modelling and forecasting of COVID-19 in Norway," Working Paper Series 19623, Department of Economics, Norwegian University of Science and Technology.
    27. Giovanni Angelini & Giuseppe Cavaliere & Enzo D'Innocenzo & Luca De Angelis, 2022. "Time-Varying Poisson Autoregression," Papers 2207.11003, arXiv.org.

  11. Boneva, Lena & Elliott, David & Kaminska, Iryna & Linton, Oliver & McLaren, Nick & Morley, Ben, 2019. "The impact of corporate QE on liquidity: evidence from the UK," Bank of England working papers 782, Bank of England, revised 23 Jul 2020.

    Cited by:

    1. Bailey, Andrew & Bridges, Jonathan & Harrison, Richard & Jones, Josh & Mankodi, Aakash, 2020. "The central bank balance sheet as a policy tool: past, present and future," Bank of England working papers 899, Bank of England.
    2. D’Amico, Stefania & Kaminska, Iryna, 2019. "Credit easing versus quantitative easing: evidence from corporate and government bond purchase programs," Bank of England working papers 825, Bank of England.
    3. Boneva, Lena & Islami, Mevlud & Schlepper, Kathi, 2021. "Liquidity in the German corporate bond market: Has the CSPP made a difference?," Discussion Papers 08/2021, Deutsche Bundesbank.
    4. Joost Bats, 2020. "Corporates dependence on banks: The impact of ECB corporate sector purchases," Working Papers 667, DNB.
    5. Christensen, Jens H.E. & Gillan, James M., 2022. "Does quantitative easing affect market liquidity?," Journal of Banking & Finance, Elsevier, vol. 134(C).
    6. Patrick Aldridge & David Cimon & Rishi Vala, 2023. "Central Bank Crisis Interventions: A Review of the Recent Literature on Potential Costs," Discussion Papers 2023-30, Bank of Canada.

  12. Tingting Cheng & Jiti Gao & Oliver Linton, 2019. "Nonparametric Predictive Regressions for Stock Return Prediction," Monash Econometrics and Business Statistics Working Papers 4/19, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.
    2. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns – The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.

  13. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Hiroyuki Kawakatsu, 2020. "Recovering Yield Curves from Dynamic Term Structure Models with Time-Varying Factors," Stats, MDPI, vol. 3(3), pages 1-46, August.

  14. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Cui, Qiurong & Xu, Yuqing & Zhang, Zhengjun & Chan, Vincent, 2021. "Max-linear regression models with regularization," Journal of Econometrics, Elsevier, vol. 222(1), pages 579-600.
    2. Hwang, Eunju & Hong, Won-Tak, 2021. "A multivariate HAR-RV model with heteroscedastic errors and its WLS estimation," Economics Letters, Elsevier, vol. 203(C).
    3. Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.

  15. Merrick Li, Z. & Linton, O., 2019. "A ReMeDI for Microstructure Noise," Cambridge Working Papers in Economics 1908, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Aleksey Kolokolov & Giulia Livieri & Davide Pirino, 2022. "Testing for Endogeneity of Irregular Sampling Schemes," CEIS Research Paper 547, Tor Vergata University, CEIS, revised 19 Dec 2022.
    2. Andersen, Torben G. & Riva, Raul & Thyrsgaard, Martin & Todorov, Viktor, 2023. "Intraday cross-sectional distributions of systematic risk," Journal of Econometrics, Elsevier, vol. 235(2), pages 1394-1418.
    3. Nabil Bouamara & Kris Boudt & S'ebastien Laurent & Christopher J. Neely, 2023. "Sluggish news reactions: A combinatorial approach for synchronizing stock jumps," Papers 2309.15705, arXiv.org.
    4. Bilel Sanhaji & Julien Chevallier, 2023. "Tracking ‘Pure’ Systematic Risk with Realized Betas for Bitcoin and Ethereum," Post-Print hal-04218488, HAL.
    5. Markus Bibinger & Nikolaus Hautsch & Alexander Ristig, 2024. "Jump detection in high-frequency order prices," Papers 2403.00819, arXiv.org.
    6. Kim, Donggyu & Song, Xinyu & Wang, Yazhen, 2022. "Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency," Journal of Multivariate Analysis, Elsevier, vol. 192(C).

  16. Hong, S-Y. & Linton, O., 2018. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Cambridge Working Papers in Economics 1877, Faculty of Economics, University of Cambridge.

    Cited by:

    1. da Silva, Murilo & Sriram, T.N. & Ke, Yuan, 2023. "Dimension reduction in time series under the presence of conditional heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    2. Trifonov, Juri, 2023. "Modeling the risk premium in the Russian stock market considering the asymmetry effect," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 71, pages 5-19.

  17. Brugler, James & Linton, Oliver & Noss, Joseph & Pedace, Lucas, 2018. "The cross-sectional spillovers of single stock circuit breakers," Bank of England working papers 759, Bank of England.

    Cited by:

    1. Kyong Shik Eom & Kyung Yoon Kwon & Sung Chae La & Jong-Ho Park, 2022. "Dynamic and Static Volatility Interruptions: Evidence from the Korean Stock Markets," JRFM, MDPI, vol. 15(3), pages 1-19, February.
    2. Wong, Kin Ming & Kong, Xiao Wei & Li, Min, 2020. "The magnet effect of circuit breakers and its interactions with price limits," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    3. Shan Lu & Jichang Zhao & Huiwen Wang, 2019. "The emergence of critical stocks in market crash," Papers 1908.07244, arXiv.org.

  18. Michael Vogt & Oliver Linton, 2018. "Multiscale clustering of nonparametric regression curves," CeMMAP working papers CWP08/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2023. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Papers 2303.13218, arXiv.org.
    2. Dong Hwan Oh & Andrew J. Patton, 2021. "Dynamic Factor Copula Models with Estimated Cluster Assignments," Finance and Economics Discussion Series 2021-029r1, Board of Governors of the Federal Reserve System (U.S.), revised 06 May 2022.
    3. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Inference of Grouped Time-Varying Network Vector Autoregression Models," Monash Econometrics and Business Statistics Working Papers 5/23, Monash University, Department of Econometrics and Business Statistics.
    4. Jia Chen, 2018. "Estimating Latent Group Structure in Time-Varying Coefficient Panel Data Models," Discussion Papers 18/15, Department of Economics, University of York.
    5. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    6. Su, Liangjun & Wang, Wuyi & Xu, Xingbai, 2023. "Identifying latent group structures in spatial dynamic panels," Journal of Econometrics, Elsevier, vol. 235(2), pages 1955-1980.
    7. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Estimation of Grouped Time-Varying Network Vector Autoregression Models," Papers 2303.10117, arXiv.org, revised Mar 2024.

  19. Linton, O. & Whang, Y-J. & Yen, Y., 2018. "The Lower Regression Function and Testing Expectation Dependence Dominance Hypotheses," Cambridge Working Papers in Economics 1880, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Denuit, Michel & Trufin, Julien & Verdebout, Thomas, 2021. "Testing for more positive expectation dependence with application to model comparison," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 163-172.
    2. Denuit, Michel & Trufin, Julien & Verdebout, Thomas, 2021. "Testing for more positive expectation dependence with application to model comparison," LIDAM Discussion Papers ISBA 2021021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  20. Chunrong Ai & Oliver Linton & Zheng Zhang, 2018. "A simple and efficient estimation method for models with nonignorable missing data," CeMMAP working papers CWP02/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Lei Wang & Wei Ma, 2021. "Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 623-647, June.
    2. Zhang, Ting & Wang, Lei, 2020. "Smoothed empirical likelihood inference and variable selection for quantile regression with nonignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

  21. Hafner, C. & Linton, O. & Tang, H., 2018. "Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case," Cambridge Working Papers in Economics 1878, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    2. Chen, Xin & Yang, Dan & Xu, Yan & Xia, Yin & Wang, Dong & Shen, Haipeng, 2023. "Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data," Journal of Econometrics, Elsevier, vol. 232(2), pages 544-564.

  22. Shujie Ma & Oliver Linton & Jiti Gao, 2018. "Estimation in semiparametric quantile factor models," CeMMAP working papers CWP07/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Katal, Ali & Mortezazadeh, Mohammad & Wang, Liangzhu (Leon), 2019. "Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations," Applied Energy, Elsevier, vol. 250(C), pages 1402-1417.

  23. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2018. "A Unified Framework for Efficient Estimation of General Treatment Models," Papers 1808.04936, arXiv.org, revised Aug 2018.

    Cited by:

    1. Jiang, Qingshan & Xu, Li & Huang, Can, 2022. "Covariates distributions balancing for continuous treatment," Economics Letters, Elsevier, vol. 217(C).
    2. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    3. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-driven Policy Learning for a Continuous Treatment," Papers 2402.02535, arXiv.org.
    4. Yukitoshi Matsushita & Taisuke Otsu & Keisuke Takahata, 2022. "Estimating density ratio of marginals to joint: Applications to causal inference," STICERD - Econometrics Paper Series 619, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    6. Lin Liu & Chang Li, 2023. "New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators," Papers 2302.08097, arXiv.org.

  24. Jia Chen & Degui Li & Oliver Linton, 2018. "A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables," Discussion Papers 18/14, Department of Economics, University of York.

    Cited by:

    1. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Papers 2012.03182, arXiv.org, revised Nov 2021.
    2. Chenlei Leng & Degui Li & Hanlin Shang & Yingcun Xia, 2024. "Covariance Function Estimation for High-Dimensional Functional Time Series with Dual Factor Structures," Papers 2401.05784, arXiv.org, revised Jan 2024.
    3. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Janeway Institute Working Papers 2208, Faculty of Economics, University of Cambridge.
    4. Jiti Gao & Fei Liu & Bin peng, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 44/20, Monash University, Department of Econometrics and Business Statistics.
    5. Xuan, Liang & Jiti, Gao & xiaodong, Gong, 2021. "Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients," MPRA Paper 108497, University Library of Munich, Germany, revised 30 May 2021.
    6. Jiti Gao & Bin Peng & Yayi Yan, 2022. "Higher-order Expansions and Inference for Panel Data Models," Papers 2205.00577, arXiv.org, revised Jun 2023.
    7. Xuan Liang & Jiti Gao & Xiaodong Gong, 2019. "Time-Varying Coefficient Spatial Autoregressive Panel Data Model with Fixed Effects," Monash Econometrics and Business Statistics Working Papers 26/19, Monash University, Department of Econometrics and Business Statistics.
    8. Jiti Gao & Bin Peng & Yayi Yan, 2022. "A Simple Bootstrap Method for Panel Data Inferences," Monash Econometrics and Business Statistics Working Papers 7/22, Monash University, Department of Econometrics and Business Statistics.
    9. Wang, Hanchao & Peng, Bin & Li, Degui & Leng, Chenlei, 2021. "Nonparametric estimation of large covariance matrices with conditional sparsity," Journal of Econometrics, Elsevier, vol. 223(1), pages 53-72.

  25. Linton, O. & Mahmoodzadeh, S., 2018. "Implications of High-Frequency Trading for Security Markets," Cambridge Working Papers in Economics 1802, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Gianluca Piero Maria Virgilio, 2019. "High-frequency trading: a literature review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(2), pages 183-208, June.
    2. Leone, Vitor & Kwabi, Frank, 2019. "High frequency trading, price discovery and market efficiency in the FTSE100," Economics Letters, Elsevier, vol. 181(C), pages 174-177.
    3. Oualid Bada & Alois Kneip & Dominik Liebl & Tim Mensinger & James Gualtieri & Robin C. Sickles, 2021. "A Wavelet Method for Panel Models with Jump Discontinuities in the Parameters," Papers 2109.10950, arXiv.org.
    4. Papavassiliou, Vassilios G. & Kinateder, Harald, 2021. "Information shares and market quality before and during the European sovereign debt crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    5. Marszk, Adam & Lechman, Ewa, 2021. "Reshaping financial systems: The role of ICT in the diffusion of financial innovations – Recent evidence from European countries," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    6. Ramos, Henrique Pinto & Perlin, Marcelo Scherer, 2020. "Does algorithmic trading harm liquidity? Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    7. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.
    8. Garg, Karan, 2021. "Machines and Markets : Assessing the Impact of Algorithmic Trading on Financial Market Efficiency," Warwick-Monash Economics Student Papers 11, Warwick Monash Economics Student Papers.

  26. Chaohua Dong & Jiti Gao & Oliver Linton, 2017. "High dimensional semiparametric moment restriction models," Monash Econometrics and Business Statistics Working Papers 17/17, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP69/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Feng, Guohua & Gao, Jiti & Peng, Bin, 2022. "An integrated panel data approach to modelling economic growth," Journal of Econometrics, Elsevier, vol. 228(2), pages 379-397.
    3. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.
    4. Chaohua Dong & Jiti Gao & Oliver Linton, 2022. "Chaohua Dong, Jiti Gao and Oliver Linton’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 707-708, July.

  27. Tingting Cheng & Jiti Gao & Oliver Linton, 2017. "Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction," Monash Econometrics and Business Statistics Working Papers 13/17, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Forecasting benchmarks of long-term stock returns via machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 221-240, February.

  28. Shujie Ma & Oliver Linton & Jiti Gao, 2017. "Estimation and inference in semiparametric quantile factor models," Monash Econometrics and Business Statistics Working Papers 8/17, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Wei, Jie & Chen, Hui, 2020. "Determining the number of factors in approximate factor models by twice K-fold cross validation," Economics Letters, Elsevier, vol. 191(C).
    2. Jie Wei & Yonghui Zhang, 2023. "Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?," Papers 2305.05934, arXiv.org.
    3. Dolado, Juan J & Chen, Liang & Gonzalo, Jesus, 2018. "Quantile Factor Models," CEPR Discussion Papers 12716, C.E.P.R. Discussion Papers.
    4. Dolado, Juan J & Chen, Liang & Gonzalo, Jesus & Pan, Haozi, 2023. "Estimation of Characteristics-based Quantile Factor Models," CEPR Discussion Papers 18115, C.E.P.R. Discussion Papers.
    5. Yang, Shuquan & Ling, Nengxiang, 2023. "Robust projected principal component analysis for large-dimensional semiparametric factor modeling," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    6. Dong, Ruipeng & Li, Daoji & Zheng, Zemin, 2021. "Parallel integrative learning for large-scale multi-response regression with incomplete outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    7. Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," The Warwick Economics Research Paper Series (TWERPS) 1230, University of Warwick, Department of Economics.
    8. Ruofan Xu & Jiti Gao & Tatsushi Oka & Yoon-Jae Whang, 2022. "Estimation of Heterogeneous Treatment Effects Using Quantile Regression with Interactive Fixed Effects," Papers 2208.03632, arXiv.org, revised Apr 2023.
    9. Dimitris Korobilis & Maximilian Schroder, 2023. "Monitoring multicountry macroeconomic risk," Papers 2305.09563, arXiv.org.
    10. Jozef Barunik & Matej Nevrla, 2022. "Common Idiosyncratic Quantile Risk," Papers 2208.14267, arXiv.org, revised Jun 2023.
    11. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    12. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.

  29. Boneva, Lena & Linton, Oliver, 2017. "A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance," Bank of England working papers 640, Bank of England.

    Cited by:

    1. Ando, Tomohiro & Bai, Jushan & Li, Kunpeng, 2022. "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 230(1), pages 20-38.
    2. Ando, Tomohiro & Bai, Jushan, 2021. "Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity," MPRA Paper 111431, University Library of Munich, Germany.
    3. Williams, Benjamin, 2020. "Nonparametric identification of discrete choice models with lagged dependent variables," Journal of Econometrics, Elsevier, vol. 215(1), pages 286-304.
    4. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Papers 2012.03182, arXiv.org, revised Nov 2021.
    5. Artūras Juodis, 2022. "A regularization approach to common correlated effects estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 788-810, June.
    6. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2021. "MCMC Conditional Maximum Likelihood for the two-way fixed-effects logit," MPRA Paper 110034, University Library of Munich, Germany.
    7. Andrea Zaghini, 2017. "The CSPP at work: yield heterogeneity and the portfolio rebalancing channel," Temi di discussione (Economic working papers) 1157, Bank of Italy, Economic Research and International Relations Area.
    8. Mingli Chen & Iv'an Fern'andez-Val & Martin Weidner, 2014. "Nonlinear Factor Models for Network and Panel Data," Papers 1412.5647, arXiv.org, revised Oct 2019.
    9. Jiti Gao & Fei Liu & Bin peng, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 44/20, Monash University, Department of Econometrics and Business Statistics.
    10. Óscar Arce & Ricardo Gimeno & Sergio Mayordomo, 2017. "Making room for the needy: the credit-reallocation effects of the ECB’s corporate QE," Working Papers 1743, Banco de España.
    11. Ye, Xiaoqing & Xu, Juan & Wu, Xiangjun, 2018. "Estimation of an unbalanced panel data Tobit model with interactive effects," Journal of choice modelling, Elsevier, vol. 28(C), pages 108-123.
    12. Eberhardt, Markus, 2018. "(At Least) Four Theories for Sovereign Default," CEPR Discussion Papers 13084, C.E.P.R. Discussion Papers.
    13. Nicola Borri & Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2024. "One Factor to Bind the Cross-Section of Returns," NBER Working Papers 32365, National Bureau of Economic Research, Inc.
    14. Mugnier, Martin & Wang, Ao, 2022. "Identification and (Fast) Estimation of Large Nonlinear Panel Models with Two-Way Fixed Effects," The Warwick Economics Research Paper Series (TWERPS) 1422, University of Warwick, Department of Economics.
    15. Mr. Markus Eberhardt & Mr. Andrea F Presbitero, 2018. "Commodity Price Movements and Banking Crises," IMF Working Papers 2018/153, International Monetary Fund.
    16. Arturas Juodis & Simon Reese, 2018. "The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation," Papers 1810.03715, arXiv.org, revised Feb 2021.
    17. Abidi, Nordine & Miquel-Flores, Ixart, 2018. "Who benefits from the corporate QE? A regression discontinuity design approach," Working Paper Series 2145, European Central Bank.
    18. Rachel Cho & Rodolphe Desbordes & Markus Eberhardt, 2022. "The causal effects of the darker side of financial development," Discussion Papers 2022-04, University of Nottingham, GEP.
    19. Liang Chen & Minyuan Zhang, 2023. "Common Correlated Effects Estimation of Nonlinear Panel Data Models," Papers 2304.13199, arXiv.org.
    20. Jiti Gao & Oliver Linton & Bin Peng, 2022. "A Nonparametric Panel Model for Climate Data with Seasonal and Spatial Variation," Monash Econometrics and Business Statistics Working Papers 9/22, Monash University, Department of Econometrics and Business Statistics.
    21. Jie Wei & Yonghui Zhang, 2022. "Panel Probit Models with Time‐Varying Individual Effects: Reestimating the Effects of Fertility on Female Labour Participation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 799-829, August.
    22. Feng, Guohua & Peng, Bin & Su, Liangjun & Yang, Thomas Tao, 2019. "Semi-parametric single-index panel data models with interactive fixed effects: Theory and practice," Journal of Econometrics, Elsevier, vol. 212(2), pages 607-622.
    23. Rodolphe Desbordes & Markus Eberhardt, 2019. "Gravity," Discussion Papers 2019-02, University of Nottingham, GEP.
    24. Lee, Yoonseok & Sul, Donggyu, 2023. "Depth-weighted means of noisy data: An application to estimating the average effect in heterogeneous panels," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    25. Feng, Qu, 2020. "Common factors and common breaks in panels: An empirical investigation," Economics Letters, Elsevier, vol. 187(C).
    26. Chen, Jia & Shin, Yongcheol & Zheng, Chaowen, 2022. "Estimation and inference in heterogeneous spatial panels with a multifactor error structure," Journal of Econometrics, Elsevier, vol. 229(1), pages 55-79.

  30. Jiti Gao & Oliver Linton & Bin Peng, 2017. "Inference on a Semiparametric Model with Global Power Law and Local Nonparametric Trends," Monash Econometrics and Business Statistics Working Papers 10/17, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Chaohua Dong & Jiti Gao & Oliver Linton & Bin Peng, 2020. "On the Time Trend of COVID-19: A Panel Data Study," Papers 2006.11060, arXiv.org, revised Jun 2020.
    2. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Papers 2012.03182, arXiv.org, revised Nov 2021.
    3. Chen, Zhihong & Xia, Huizhu, 2020. "Trend instrumental variable regression with an application to the US New Keynesian Phillips Curve," Economic Modelling, Elsevier, vol. 93(C), pages 595-604.

  31. Auld, T. & Linton, O., 2017. "The Behaviour of Betting and Currency Markets on the Night of the EU Referendum," Cambridge Working Papers in Economics 1750, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Manamba Epaphra & Khatibu Kazungu, 2021. "Efficiency of Tanzania's foreign exchange market," African Development Review, African Development Bank, vol. 33(2), pages 368-381, June.
    2. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    3. Wiśniowski, Arkadiusz & Bijak, Jakub & Forster, Jonathan J. & Smith, Peter W.F., 2019. "Hierarchical model for forecasting the outcomes of binary referenda," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 90-103.
    4. Auld, T., 2022. "Betting and financial markets are cointegrated on election night," Cambridge Working Papers in Economics 2263, Faculty of Economics, University of Cambridge.
    5. P. Manasse & G. Moramarco & G. Trigilia, 2020. "Exchange Rates and Political Uncertainty: The Brexit Case," Working Papers wp1141, Dipartimento Scienze Economiche, Universita' di Bologna.
    6. Wael Bousselmi & Patrick Sentis & Marc Willinger, 2018. "Impact of the Brexit vote announcement on long-run market performance," CEE-M Working Papers hal-01954920, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
    7. Facundo Albornoz & Jake Bradley & Silvia Sonderegger, 2020. "The Brexit referendum and the rise in hate crime; conforming to the new norm," Discussion Papers 2020-06, Nottingham Interdisciplinary Centre for Economic and Political Research (NICEP).
    8. Facundo Albornoz & Jake Bradley & Silvia Sonderegger, 2022. "Updating the Social Norm: the Case of Hate Crime after the Brexit Referendum," Working Papers 203, Red Nacional de Investigadores en Economía (RedNIE).

  32. Chaohua Dong & Oliver Linton, 2017. "Additive nonparametric models with time variable and both stationary and nonstationary regressions," CeMMAP working papers CWP59/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Tingting Cheng & Chaohua Dong & Jiti Gao & Oliver Linton, 2022. "GMM Estimation for High-Dimensional Panel Data Models," Monash Econometrics and Business Statistics Working Papers 11/22, Monash University, Department of Econometrics and Business Statistics.
    2. Ruiqi Liu & Ben Boukai & Zuofeng Shang, 2019. "Statistical Inference on Partially Linear Panel Model under Unobserved Linearity," Papers 1911.08830, arXiv.org.
    3. Bin Peng & Liangjun Su & Joakim Westerlund & Yanrong Yang, 2021. "Interactive Effects Panel Data Models with General Factors and Regressors," Papers 2111.11506, arXiv.org.
    4. Fei Liu & Jiti Gao & Yanrong Yang, 2019. "Nonparametric Estimation in Panel Data Models with Heterogeneity and Time Varyingness," Monash Econometrics and Business Statistics Working Papers 24/19, Monash University, Department of Econometrics and Business Statistics.
    5. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
    7. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2023. "Robust M-Estimation for Additive Single-Index Cointegrating Time Series Models," Monash Econometrics and Business Statistics Working Papers 2/23, Monash University, Department of Econometrics and Business Statistics.
    8. Cheng, T. & Gao, J. & Linton, O., 2019. "Nonparametric Predictive Regressions for Stock Return Prediction," Cambridge Working Papers in Economics 1932, Faculty of Economics, University of Cambridge.
    9. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    10. Tu, Yundong & Wang, Ying, 2022. "Spurious functional-coefficient regression models and robust inference with marginal integration," Journal of Econometrics, Elsevier, vol. 229(2), pages 396-421.
    11. Qiying Wang & Peter C. B. Phillips, 2022. "A General Limit Theory for Nonlinear Functionals of Nonstationary Time Series," Cowles Foundation Discussion Papers 2337, Cowles Foundation for Research in Economics, Yale University.
    12. Feng, Guohua & Gao, Jiti & Peng, Bin, 2022. "An integrated panel data approach to modelling economic growth," Journal of Econometrics, Elsevier, vol. 228(2), pages 379-397.
    13. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," Journal of Econometrics, Elsevier, vol. 216(1), pages 175-191.
    14. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation of Semiparametric Multi-Index Models Using Deep Neural Networks," Monash Econometrics and Business Statistics Working Papers 21/23, Monash University, Department of Econometrics and Business Statistics.
    15. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2021. "Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice," Monash Econometrics and Business Statistics Working Papers 18/21, Monash University, Department of Econometrics and Business Statistics.
    16. Pourkhanali, Armin & Tafakori, Laleh & Bee, Marco, 2023. "Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect," International Review of Financial Analysis, Elsevier, vol. 89(C).
    17. Peng, Zhen & Dong, Chaohua, 2022. "Augmented cointegrating linear models with possibly strongly correlated stationary and nonstationary regressors," Finance Research Letters, Elsevier, vol. 47(PB).
    18. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," LSE Research Online Documents on Economics 103830, London School of Economics and Political Science, LSE Library.
    19. Guohua Feng & Jiti Gao & Bin Peng, 2019. "An Integrated Panel Data Approach to Modelling Economic Growth," Monash Econometrics and Business Statistics Working Papers 6/19, Monash University, Department of Econometrics and Business Statistics.
    20. Ayman Mnasri & Zouhair Mrabet & Mouyad Alsamara, 2023. "A new quadratic asymmetric error correction model: does size matter?," Empirical Economics, Springer, vol. 65(1), pages 33-64, July.
    21. Feng, Guohua & Peng, Bin & Su, Liangjun & Yang, Thomas Tao, 2019. "Semi-parametric single-index panel data models with interactive fixed effects: Theory and practice," Journal of Econometrics, Elsevier, vol. 212(2), pages 607-622.
    22. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2021. "Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice," Papers 2111.02023, arXiv.org.
    23. Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.
    24. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.

  33. Noss, Joseph & Pedace, Lucas & Tobek, Ondrej & Linton, Oliver & Crowley-Reidy, Liam, 2017. "The October 2016 sterling flash episode: when liquidity disappeared from one of the world’s most liquid markets," Bank of England working papers 687, Bank of England.

    Cited by:

    1. Oliver Linton & Soheil Mahmoodzadeh, 2018. "Implications of high-frequency trading for security markets," CeMMAP working papers CWP06/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. David‐Jan Jansen, 2021. "The International Spillovers of the 2010 U.S. Flash Crash," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(6), pages 1573-1586, September.
    3. Antoine Bouveret & Martin Haferkorn & Gaetano Marseglia & Onofrio Panzarino, 2022. "Flash crashes on sovereign bond markets – EU evidence," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 20, Bank of Italy, Directorate General for Markets and Payment System.

  34. Linton, O. & Wu, J., 2016. "A coupled component GARCH model for intraday and overnight volatility," Cambridge Working Papers in Economics 1671, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Oliver Linton & Soheil Mahmoodzadeh, 2018. "Implications of high-frequency trading for security markets," CeMMAP working papers CWP06/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    3. Anne Opschoor & André Lucas, 2019. "Observation-driven Models for Realized Variances and Overnight Returns," Tinbergen Institute Discussion Papers 19-052/IV, Tinbergen Institute.
    4. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.

  35. Xiaohong Chen & Oliver Linton & Stefan Schneeberger, 2016. "Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model," Cambridge Working Papers in Economics 1620, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Li, Zhiyong & Lambe, Brendan & Adegbite, Emmanuel, 2018. "New bid-ask spread estimators from daily high and low prices," International Review of Financial Analysis, Elsevier, vol. 60(C), pages 69-86.

  36. Seok Young Hong & Oliver Linton, 2016. "Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in?finite order," CeMMAP working papers CWP53/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Yuliana Linke & Igor Borisov & Pavel Ruzankin & Vladimir Kutsenko & Elena Yarovaya & Svetlana Shalnova, 2022. "Universal Local Linear Kernel Estimators in Nonparametric Regression," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
    2. Eric Auerbach, 2019. "Identification and Estimation of a Partially Linear Regression Model using Network Data," Papers 1903.09679, arXiv.org, revised Jun 2021.

  37. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2015. "An investigation into multivariate variance ratio statistics and their application to stock market predictability," CeMMAP working papers CWP13/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Atanda Mustapha Saidi, 2017. "Working Paper 273 - Stock (Mis)pricing and investment dynamics in Africa," Working Paper Series 2390, African Development Bank.
    2. O'Callaghan, Patrick, 2016. "Measuring utility without mixing apples and oranges and eliciting beliefs about stock prices," MPRA Paper 69363, University Library of Munich, Germany.

  38. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers CWP06/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Stéphane Bonhomme & Thibaut Lamadon & Elena Manresa, 2017. "Discretizing unobserved heterogeneity," IFS Working Papers W17/03, Institute for Fiscal Studies.
    2. Hafner, Christian & Walders, Fabian, 2017. "Heterogeneous Liquidity Effects in Corporate Bond Spreads," LIDAM Reprints ISBA 2017037, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Jiti Gao & Kai Xia, 2017. "Heterogeneous panel data models with cross-sectional dependence," Monash Econometrics and Business Statistics Working Papers 16/17, Monash University, Department of Econometrics and Business Statistics.

  39. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric Dynamic Portfolio Choice with Multiple Conditioning Variables," Discussion Papers 15/01, Department of Economics, University of York.

    Cited by:

    1. Jingwen Tu & Hu Yang & Chaohui Guo & Jing Lv, 2021. "Model averaging marginal regression for high dimensional conditional quantile prediction," Statistical Papers, Springer, vol. 62(6), pages 2661-2689, December.
    2. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric Model Averaging of Ultra-High Dimensional Time Series," Discussion Papers 15/18, Department of Economics, University of York.
    3. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
    4. Chen, Jia & Li, Degui & Linton, Oliver, 2019. "A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables," Journal of Econometrics, Elsevier, vol. 212(1), pages 155-176.

  40. Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series," CeMMAP working papers CWP06/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Syed Jawad Hussain Shahzad & Thi Hong Van Hoang & Jose Arreola-Hernandez, 2019. "Risk spillovers between large banks and the financial sector: Asymmetric evidence from Europe," Post-Print hal-02129104, HAL.
    2. Klochkov, Yegor & Härdle, Wolfgang Karl & Xu, Xiu, 2019. "Localizing Multivariate CAViaR," IRTG 1792 Discussion Papers 2019-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2019. "Return spillovers around the globe: A network approach," Economic Modelling, Elsevier, vol. 77(C), pages 133-146.
    4. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2020. "The role of real estate uncertainty in predicting US home sales growth: evidence from a quantiles-based Bayesian model averaging approach," Applied Economics, Taylor & Francis Journals, vol. 52(5), pages 528-536, January.
    5. Guo, Dong & Zhou, Peng, 2021. "Green bonds as hedging assets before and after COVID: A comparative study between the US and China," Energy Economics, Elsevier, vol. 104(C).
    6. Dai, Zhifeng & Zhu, Junxin & Zhang, Xinhua, 2022. "Time-frequency connectedness and cross-quantile dependence between crude oil, Chinese commodity market, stock market and investor sentiment," Energy Economics, Elsevier, vol. 114(C).
    7. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    8. Atil, Ahmed & Nawaz, Kishwar & Lahiani, Amine & Roubaud, David, 2020. "Are natural resources a blessing or a curse for financial development in Pakistan? The importance of oil prices, economic growth and economic globalization," Resources Policy, Elsevier, vol. 67(C).
    9. Bouri, Elie & Gupta, Rangan & Lau, Chi Keung Marco & Roubaud, David & Wang, Shixuan, 2018. "Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 297-307.
    10. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    11. Jozef Barun'ik & Tobias Kley, 2015. "Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables," Papers 1510.06946, arXiv.org, revised Dec 2018.
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    132. Nikolaos A. Kyriazis, 2020. "Is Bitcoin Similar to Gold? An Integrated Overview of Empirical Findings," JRFM, MDPI, vol. 13(5), pages 1-19, May.
    133. Uribe, Jorge M. & Guillen, Montserrat & Mosquera-López, Stephania, 2018. "Uncovering the nonlinear predictive causality between natural gas and electricity prices," Energy Economics, Elsevier, vol. 74(C), pages 904-916.
    134. Ando, Tomohiro & Bai, Jushan, 2018. "Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity," MPRA Paper 88765, University Library of Munich, Germany.
    135. Todorova, Neda, 2017. "The intraday directional predictability of large Australian stocks: A cross-quantilogram analysis," Economic Modelling, Elsevier, vol. 64(C), pages 221-230.
    136. Maghyereh, Aktham & Abdoh, Hussein, 2020. "Tail dependence between Bitcoin and financial assets: Evidence from a quantile cross-spectral approach," International Review of Financial Analysis, Elsevier, vol. 71(C).
    137. Deev, Oleg & Lyócsa, Štefan, 2020. "Connectedness of financial institutions in Europe: A network approach across quantiles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    138. Deev, Oleg & Lyócsa, Štefan & Výrost, Tomáš, 2022. "The looming crisis in the Chinese stock market? Left-tail exposure analysis of Chinese stocks to Evergrande," Finance Research Letters, Elsevier, vol. 49(C).
    139. Tiwari, Aviral Kumar & Trabelsi, Nader & Abakah, Emmanuel Joel Aikins & Nasreen, Samia & Lee, Chien-Chiang, 2023. "An empirical analysis of the dynamic relationship between clean and dirty energy markets," Energy Economics, Elsevier, vol. 124(C).
    140. Zhang, Jiahao & Chen, Xiaodan & Wei, Yu & Bai, Lan, 2023. "Does the connectedness among fossil energy returns matter for renewable energy stock returns? Fresh insights from the Cross-Quantilogram analysis," International Review of Financial Analysis, Elsevier, vol. 88(C).
    141. Chishti, Muhammad Zubair & Khalid, Ali Awais & Sana, Moniba, 2023. "Conflict vs sustainability of global energy, agricultural and metal markets: A lesson from Ukraine-Russia war," Resources Policy, Elsevier, vol. 84(C).
    142. Shen, Yifan & Shi, Xunpeng & Variam, Hari Malamakkavu Padinjare, 2018. "Risk transmission mechanism between energy markets: A VAR for VaR approach," Energy Economics, Elsevier, vol. 75(C), pages 377-388.
    143. Emmanuel Joel Aikins Abakah & Aviral Kumar Tiwari & Chi‐Chuan Lee & Matthew Ntow‐Gyamfi, 2023. "Quantile price convergence and spillover effects among Bitcoin, Fintech, and artificial intelligence stocks," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 187-205, March.
    144. Shahzad, Syed Jawad Hussain & Hernandez, Jose Areola & Rehman, Mobeen Ur & Al-Yahyaee, Khamis Hamed & Zakaria, Muhammad, 2018. "A global network topology of stock markets: Transmitters and receivers of spillover effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2136-2153.
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    146. Altınkeski, Buket Kırcı & Cevik, Emrah Ismail & Dibooglu, Sel & Kutan, Ali M., 2022. "Financial stress transmission between the U.S. and the Euro Area," Journal of Financial Stability, Elsevier, vol. 60(C).
    147. Tian, Tingting & Lai, Kee-hung & Wong, Christina W.Y., 2022. "Connectedness mechanisms in the “Carbon-Commodity-Finance” system: Investment and management policy implications for emerging economies," Energy Policy, Elsevier, vol. 169(C).
    148. Burak Büyükoğlu, 2022. "Analysis of the Relationship between Green Bonds and Equity Markets by Cross-Quantilogram Method," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(4), pages 855-868.
    149. Shahzad, Syed Jawad Hussain & Rahman, Md Lutfur & Lucey, Brian M. & Uddin, Gazi Salah, 2021. "Re-examining the real option characteristics of gold for gold mining companies," Resources Policy, Elsevier, vol. 70(C).
    150. Naeem, Muhammad Abubakr & Mbarki, Imen & Shahzad, Syed Jawad Hussain, 2021. "Predictive role of online investor sentiment for cryptocurrency market: Evidence from happiness and fears," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 496-514.
    151. Huang, Jionghao & Li, Ziruo & Xia, Xiaohua, 2021. "Network diffusion of international oil volatility risk in China's stock market: Quantile interconnectedness modelling and shock decomposition analysis," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 1-39.
    152. Mikhail Stolbov & Maria Shchepeleva, 2021. "Macrofinancial linkages in Europe: Evidence from quantile local projections," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5557-5569, October.
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    155. Uddin, Gazi Salah & Rahman, Md Lutfur & Hedström, Axel & Ahmed, Ali, 2019. "Cross-quantilogram-based correlation and dependence between renewable energy stock and other asset classes," Energy Economics, Elsevier, vol. 80(C), pages 743-759.
    156. Ji Ho Kwon, 2021. "On the factors of Bitcoin’s value at risk," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    157. Ji, Qiang & Zhang, Dayong & Zhao, Yuqian, 2020. "Searching for safe-haven assets during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 71(C).
    158. Shahzad, Syed Jawad Hussain & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav & Lucey, Brian, 2019. "Is Bitcoin a better safe-haven investment than gold and commodities?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 322-330.
    159. Dragos HURU & Ioana MANAFI & Ionut PANDELICA & Marilena Carmen UZLAU, 2022. "Nonlinear Dependencies between Green Bonds and General Financial Market Indices," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 169-181, December.
    160. Lei, Heng & Xue, Minggao & Liu, Huiling & Ye, Jing, 2023. "Precious metal as a safe haven for global ESG stocks: Portfolio implications for socially responsible investing," Resources Policy, Elsevier, vol. 80(C).
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    162. Bouri, Elie & Lien, Donald & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Directional predictability of implied volatility: From crude oil to developed and emerging stock markets," Finance Research Letters, Elsevier, vol. 27(C), pages 65-79.
    163. Adewuyi, Adeolu O. & Awodumi, Olabanji B. & Abodunde, Temitope T., 2019. "Analysing the gold-stock nexus using VARMA-BEKK-AGARCH and Quantile regression models: New evidence from South Africa and Nigeria," Resources Policy, Elsevier, vol. 61(C), pages 348-362.
    164. Georgios Bampinas & Panagiotis Konstantinou & Theodore Panagiotidis, 2021. "Reassessing the inflation uncertainty‐inflation relationship in the tails," Bulletin of Economic Research, Wiley Blackwell, vol. 73(4), pages 508-534, October.
    165. Nahla Samargandi & Kazi Sohag, 2022. "Oil Price Shocks to Foreign Assets and Liabilities in Saudi Arabia under Pegged Exchange Rate," Mathematics, MDPI, vol. 10(24), pages 1-15, December.
    166. Heejoon Han, 2016. "Quantile Dependence between Stock Markets and its Application in Volatility Forecasting," Papers 1608.07193, arXiv.org.
    167. Tan Le & Franck Martin & Duc Nguyen, 2018. "Dynamic connectedness of global currencies: a conditional Granger-causality approach," Working Papers hal-01806733, HAL.
    168. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Shao, Xuefeng & Le, TN-Lan & Gyamfi, Matthew Ntow, 2023. "Financial technology stocks, green financial assets, and energy markets: A quantile causality and dependence analysis," Energy Economics, Elsevier, vol. 118(C).
    169. Thobekile Qabhobho & Anokye M. Adam & Emmanuel Asafo-Adjei, 2023. "Do Local and International Shocks Matter in the Interconnectedness amid Exchange Rates and Energy Commodities? Insights into BRICS Economies," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 666-678, November.
    170. Naeem, Muhammad Abubakr & Nguyen, Thi Thu Ha & Nepal, Rabindra & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021. "Asymmetric relationship between green bonds and commodities: Evidence from extreme quantile approach," Finance Research Letters, Elsevier, vol. 43(C).
    171. Naeem, Muhammad Abubakr & Karim, Sitara & Abrar, Afsheen & Yarovaya, Larisa & Shah, Adil Ahmad, 2023. "Non-linear relationship between oil and cryptocurrencies: Evidence from returns and shocks," International Review of Financial Analysis, Elsevier, vol. 89(C).
    172. Stenvall, David & Hedström, Axel & Yoshino, Naoyuki & Uddin, Gazi Salah & Taghizadeh-Hesary, Farhad, 2022. "Nonlinear tail dependence between the housing and energy markets," Energy Economics, Elsevier, vol. 106(C).
    173. Urom, Christian & Abid, Ilyes & Guesmi, Khaled & Chevallier, Julien, 2020. "Quantile spillovers and dependence between Bitcoin, equities and strategic commodities," Economic Modelling, Elsevier, vol. 93(C), pages 230-258.
    174. Ye, Wuyi & Li, Mingge & Wu, Yuehua, 2022. "A novel estimation of time-varying quantile correlation for financial contagion detection," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    175. Syed Jawad Hussain Shahzad & Elie Bouri & Mobeen Ur Rehman & David Roubaud, 2022. "The hedge asset for BRICS stock markets: Bitcoin, gold or VIX," The World Economy, Wiley Blackwell, vol. 45(1), pages 292-316, January.
    176. Shahzad, Syed Jawad Hussain & Rehman, Mobeen Ur & Jammazi, Rania, 2019. "Spillovers from oil to precious metals: Quantile approaches," Resources Policy, Elsevier, vol. 61(C), pages 508-521.
    177. Kołodziejczyk, Hanna, 2023. "Stablecoins as diversifiers, hedges and safe havens: A quantile coherency approach," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    178. Duan, Kun & Ren, Xiaohang & Wen, Fenghua & Chen, Jinyu, 2023. "Evolution of the information transmission between Chinese and international oil markets: A quantile-based framework," Journal of Commodity Markets, Elsevier, vol. 29(C).
    179. Alomari, Mohammad & Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Extreme return spillovers and connectedness between crude oil and precious metals futures markets: Implications for portfolio management," Resources Policy, Elsevier, vol. 79(C).
    180. Hartley, Robert Paul & Lamarche, Carlos & Ziliak, James P., 2023. "Bootstrapping quantile correlations with an application for income status across generations," Economics Letters, Elsevier, vol. 228(C).
    181. Zhao, Hong & Li, Jiayi & Lei, Yiqing & Zhou, Mingming, 2022. "Risk spillover of banking across regions: Evidence from the belt and road countries," Emerging Markets Review, Elsevier, vol. 52(C).
    182. Montes-Rojas, Gabriel, 2017. "Reduced form vector directional quantiles," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 20-30.
    183. Kwon, Ji Ho, 2020. "Tail behavior of Bitcoin, the dollar, gold and the stock market index," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 67(C).
    184. Román Ferrer & Rafael Benítez & Vicente J. Bolós, 2021. "Interdependence between Green Financial Instruments and Major Conventional Assets: A Wavelet-Based Network Analysis," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    185. Ji-Eun Choi & Dong Wan Shin, 2022. "Quantile correlation coefficient: a new tail dependence measure," Statistical Papers, Springer, vol. 63(4), pages 1075-1104, August.
    186. Baumöhl, Eduard & Bouri, Elie & Hoang, Thi-Hong-Van & Shahzad, Syed Jawad Hussain & Výrost,Tomáš, 2020. "From physical to financial contagion: the COVID-19 pandemic and increasing systemic risk among banks," EconStor Preprints 218944, ZBW - Leibniz Information Centre for Economics.
    187. Sohag, Kazi & Hammoudeh, Shawkat & Elsayed, Ahmed H. & Mariev, Oleg & Safonova, Yulia, 2022. "Do geopolitical events transmit opportunity or threat to green markets? Decomposed measures of geopolitical risks," Energy Economics, Elsevier, vol. 111(C).
    188. Md. Monirul Islam & Kazi Sohag & Faheem ur Rehman, 2022. "Do Geopolitical Tensions and Economic Policy Uncertainties Reorient Mineral Imports in the USA? A Fat-Tailed Data Analysis Using Novel Quantile Approaches," Mathematics, MDPI, vol. 11(1), pages 1-25, December.
    189. Ali, Fahad & Bouri, Elie & Naifar, Nader & Shahzad, Syed Jawad Hussain & AlAhmad, Mohammad, 2022. "An examination of whether gold-backed Islamic cryptocurrencies are safe havens for international Islamic equity markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    190. Yahya, Muhammad & Dutta, Anupam & Bouri, Elie & Wadström, Christoffer & Uddin, Gazi Salah, 2022. "Dependence structure between the international crude oil market and the European markets of biodiesel and rapeseed oil," Renewable Energy, Elsevier, vol. 197(C), pages 594-605.
    191. Chang, Jinyuan & Jiang, Qing & Shao, Xiaofeng, 2023. "Testing the martingale difference hypothesis in high dimension," Journal of Econometrics, Elsevier, vol. 235(2), pages 972-1000.
    192. Lindman, Sebastian & Tuvhag, Tom & Jayasekera, Ranadeva & Uddin, Gazi Salah & Troster, Victor, 2020. "Market Impact on financial market integration: Cross-quantilogram analysis of the global impact of the euro," Journal of Empirical Finance, Elsevier, vol. 56(C), pages 42-73.
    193. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.
    194. Cho, Dooyeon & Han, Heejoon, 2021. "The tail behavior of safe haven currencies: A cross-quantilogram analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    195. Kazi Sohag & Anna Gainetdinova & Shawkat Hammoudeh & Riad Shams, 2022. "Dynamic Connectedness among Vaccine Companies’ Stock Prices: Before and after Vaccines Released," Mathematics, MDPI, vol. 10(15), pages 1-26, August.
    196. Borg, Elin & Kits, Ilya & Junttila, Juha & Uddin, Gazi Salah, 2022. "Dependence between renewable energy related critical metal futures and producer equity markets across varying market conditions," Renewable Energy, Elsevier, vol. 190(C), pages 879-892.
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    201. Derick David Quintino & Heloisa Lee Burnquist & Paulo Jorge Silveira Ferreira, 2021. "Carbon Emissions and Brazilian Ethanol Prices: Are They Correlated? An Econophysics Study," Sustainability, MDPI, vol. 13(22), pages 1-18, November.

  41. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2014. "Multivariate variance ratio statistics," CeMMAP working papers CWP29/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Neil Kellard & Denise Osborn & Jerry Coakley & John C. Nankervis & Periklis Kougoulis & Jerry Coakley, 2015. "Generalized Variance-Ratio Tests in the Presence of Statistical Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 687-705, September.

  42. James Brugler & Oliver Linton, 2014. "Single stock circuit breakers on the London Stock Exchange: do they improve subsequent market quality?," CeMMAP working papers CWP07/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Leal, Sandrine Jacob & Napoletano, Mauro, 2019. "Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 15-41.
    2. Kun Li, 2019. "Do Circuit Breakers Impede Trading Behavior? A Study In Chinese Financial Market," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(05), pages 1-18, December.
    3. Imtiaz Mohammad Sifat & Azhar Mohamad, 2019. "Circuit breakers as market stability levers: A survey of research, praxis, and challenges," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 1130-1169, July.
    4. Sifat, Imtiaz Mohammad & Mohamad, Azhar, 2018. "Trading aggression when price limit hits are imminent: NARDL based intraday investigation of magnet effect," Journal of Behavioral and Experimental Finance, Elsevier, vol. 20(C), pages 1-8.
    5. Sifat, Imtiaz Mohammad & Mohamad, Azhar, 2020. "A survey on the magnet effect of circuit breakers in financial markets," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 138-151.
    6. Laly, Floris & Petitjean, Mikael, 2021. "Mini flash crashes: Review, taxonomy and policy responses," LIDAM Reprints LFIN 2021017, Université catholique de Louvain, Louvain Finance (LFIN).
    7. Brugler, James, 2015. "Into the light: dark pool trading and intraday market quality on the primary exchange," Bank of England working papers 545, Bank of England.

  43. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Myrto Kasioumi & Thanasis Stengos, 2020. "The Environmental Kuznets Curve with Recycling: A Partially Linear Semiparametric Approach," JRFM, MDPI, vol. 13(11), pages 1-26, November.
    2. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    3. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "The effect of fragmentation in trading on market quality in the UK equity market," CeMMAP working papers 42/13, Institute for Fiscal Studies.
    4. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric Model Averaging of Ultra-High Dimensional Time Series," Discussion Papers 15/18, Department of Economics, University of York.
    5. Timothy Neal, 2016. "Multidimensional Parameter Heterogeneity in Panel Data Models," Discussion Papers 2016-15, School of Economics, The University of New South Wales.
    6. Sinem Hacıoğlu Hoke & George Kapetanios, 2021. "Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 125-150, January.
    7. Jia Chen & Degui Li & Yingcun Xia, 2015. "New Semiparametric Estimation Procedure for Functional Coefficient Longitudinal Data Models," Discussion Papers 15/17, Department of Economics, University of York.
    8. Badi Baltagi & Georges Bresson & Jean-Michel Etienne, 2020. "Growth Empirics: A Bayesian Semiparametric Model with Random Coefficients for a Panel of OECD Countries," Center for Policy Research Working Papers 229, Center for Policy Research, Maxwell School, Syracuse University.
    9. Jia Chen, 2018. "Estimating Latent Group Structure in Time-Varying Coefficient Panel Data Models," Discussion Papers 18/15, Department of Economics, University of York.
    10. Lee, Jungyoon & Robinson, Peter M., 2015. "Panel nonparametric regression with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 346-362.
    11. Archer Gong Zhang & Jiahua Chen, 2023. "Optimal Estimation under a Semiparametric Density Ratio Model," Papers 2309.09103, arXiv.org.
    12. Lee, Jungyoon & Robinson, Peter, 2015. "Panel nonparametric regression with fixed effects," LSE Research Online Documents on Economics 61431, London School of Economics and Political Science, LSE Library.
    13. Hacioglu Hoke, Sinem & Kapetanios, George, 2017. "Common correlated effect cross-sectional dependence corrections for non-linear conditional mean panel models," Bank of England working papers 683, Bank of England.
    14. Chen, Bin & Huang, Liquan, 2018. "Nonparametric testing for smooth structural changes in panel data models," Journal of Econometrics, Elsevier, vol. 202(2), pages 245-267.
    15. Jiti Gao & Kai Xia, 2017. "Heterogeneous panel data models with cross-sectional dependence," Monash Econometrics and Business Statistics Working Papers 16/17, Monash University, Department of Econometrics and Business Statistics.
    16. Gao, Jiti & Xia, Kai & Zhu, Huanjun, 2020. "Heterogeneous panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 219(2), pages 329-353.
    17. Chen, Jia & Li, Degui & Xia, Yingcun, 2019. "Estimation of a rank-reduced functional-coefficient panel data model with serial correlation," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 456-479.

  44. HAFNER, Christian & LINTON, Oliver, 2013. "An almost closed form estimator for the EGARCH model," LIDAM Discussion Papers CORE 2013022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. CORNUEJOLS, Gérard & WOLSEY, Laurence & YILDIZ, Sercan, 2013. "Sufficiency of cut-generating functions," LIDAM Discussion Papers CORE 2013027, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Chiara Canta & Marie-Louise Leroux, 2016. "Public and Private Hospitals, Congestion, and Redistribution," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 18(1), pages 42-66, February.
    3. Bocart, Fabian Y.R.P. & Hafner, Christian, 2015. "Fair Revaluation of Wine as an Investment," LIDAM Reprints ISBA 2015040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. GOERTZ, Johanna & MANIQUET, François, 2013. "Large elections with multiple alternatives: a Condorcet Jury Theorem and inefficient equilibria," LIDAM Discussion Papers CORE 2013023, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. M. Angeles Carnero Fernández & Ana Pérez Espartero, 2018. "Outliers and misleading leverage effect in asymmetric GARCH-type models," Working Papers. Serie AD 2018-01, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    6. MLINAR, Tanja B. & CHEVALIER, Philippe, 2013. "Pooling in manufacturing: do opposites attract?," LIDAM Discussion Papers CORE 2013040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. HAFNER Christian, & KYRIAKOPOULOU Dimitra,, 2019. "Exponential-type GARCH models with linear-in-variance risk premium," LIDAM Discussion Papers CORE 2019013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. DEMOS Antonis, & KYRIAKOPOULOU Dimitra,, 2018. "Finite sample theory and bias correction of maximum likelihood estimators in the EGARCH model," LIDAM Discussion Papers CORE 2018007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. NESTEROV, Yurii, 2013. "Universal gradient methods for convex optimization problems," LIDAM Discussion Papers CORE 2013026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Prono Todd, 2018. "Closed-form estimators for finite-order ARCH models as simple and competitive alternatives to QMLE," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-25, December.
    11. Ahsan, Md. Nazmul & Dufour, Jean-Marie, 2021. "Simple estimators and inference for higher-order stochastic volatility models," Journal of Econometrics, Elsevier, vol. 224(1), pages 181-197.

  45. Hafner C. & Linton, O., 2013. "An Almost Closed Form Estimator for the EGARCH," LIDAM Discussion Papers ISBA 2013010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. CORNUEJOLS, Gérard & WOLSEY, Laurence & YILDIZ, Sercan, 2013. "Sufficiency of cut-generating functions," LIDAM Discussion Papers CORE 2013027, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Hafner, Christian & Linton, Oliver, 2017. "An Almost Closed Form Estimator For The EGARCH Model," LIDAM Reprints ISBA 2017040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Chiara Canta & Marie-Louise Leroux, 2016. "Public and Private Hospitals, Congestion, and Redistribution," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 18(1), pages 42-66, February.
    4. Bocart, Fabian Y.R.P. & Hafner, Christian, 2015. "Fair Revaluation of Wine as an Investment," LIDAM Reprints ISBA 2015040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. GOERTZ, Johanna & MANIQUET, François, 2013. "Large elections with multiple alternatives: a Condorcet Jury Theorem and inefficient equilibria," LIDAM Discussion Papers CORE 2013023, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. MLINAR, Tanja B. & CHEVALIER, Philippe, 2013. "Pooling in manufacturing: do opposites attract?," LIDAM Discussion Papers CORE 2013040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. NESTEROV, Yurii, 2013. "Universal gradient methods for convex optimization problems," LIDAM Discussion Papers CORE 2013026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

  46. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2013. "A nonparametric test of a strong leverage hypothesis," CeMMAP working papers CWP28/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Ekaterina Smetanina, 2017. "Real-Time GARCH," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 561-601.
    2. Horpestad, Jone B. & Lyócsa, Štefan & Molnár, Peter & Olsen, Torbjørn B., 2019. "Asymmetric volatility in equity markets around the world," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 540-554.
    3. Li, Tong & Oka, Tatsushi, 2015. "Set identification of the censored quantile regression model for short panels with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 363-377.
    4. M. MALLIKARJUNA & R. Prabhakara RAO, 2019. "Volatility experience of major world stock markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(621), W), pages 35-52, Winter.
    5. Muhammad Surajo Sanusi, 2017. "Investigating the sources of Black’s leverage effect in oil and gas stocks," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1318812-131, January.
    6. Yen, Yu-Min & Yen, Tso-Jung, 2021. "Testing forecast accuracy of expectiles and quantiles with the extremal consistent loss functions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 733-758.

  47. Oliver Linton & Qiying Wang, 2013. "Non-parametric transformation regression with non-stationary data," CeMMAP working papers CWP16/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Dong, Chaohua & Linton, Oliver, 2018. "Additive nonparametric models with time variable and both stationary and nonstationary regressors," Journal of Econometrics, Elsevier, vol. 207(1), pages 212-236.
    2. Wang, Qiying & Wu, Dongsheng & Zhu, Ke, 2018. "Model checks for nonlinear cointegrating regression," Journal of Econometrics, Elsevier, vol. 207(2), pages 261-284.
    3. Yuliana Linke & Igor Borisov & Pavel Ruzankin & Vladimir Kutsenko & Elena Yarovaya & Svetlana Shalnova, 2022. "Universal Local Linear Kernel Estimators in Nonparametric Regression," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
    4. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
    5. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," Journal of Econometrics, Elsevier, vol. 216(1), pages 175-191.
    6. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," LSE Research Online Documents on Economics 103830, London School of Economics and Political Science, LSE Library.
    7. Ayman Mnasri & Zouhair Mrabet & Mouyad Alsamara, 2023. "A new quadratic asymmetric error correction model: does size matter?," Empirical Economics, Springer, vol. 65(1), pages 33-64, July.

  48. Bonsoo Koo & Oliver Linton, 2013. "Let's get LADE: robust estimation of semiparametric multiplicative volatility models," CeMMAP working papers CWP11/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. David T. Frazier & Bonsoo Koo, 2020. "Indirect Inference for Locally Stationary Models," Monash Econometrics and Business Statistics Working Papers 30/20, Monash University, Department of Econometrics and Business Statistics.
    2. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    3. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.

  49. Heather Battey & Oliver Linton, 2013. "Nonparametric estimation of multivariate elliptic densities via finite mixture sieves," CeMMAP working papers CWP41/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    2. Linton, O. & Wu, J., 2016. "A coupled component GARCH model for intraday and overnight volatility," Cambridge Working Papers in Economics 1671, Faculty of Economics, University of Cambridge.
    3. Eckhard Liebscher & Wolf-Dieter Richter, 2016. "Estimation of Star-Shaped Distributions," Risks, MDPI, vol. 4(4), pages 1-37, November.
    4. Francq, C. & Jiménez-Gamero, M.D. & Meintanis, S.G., 2017. "Tests for conditional ellipticity in multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 196(2), pages 305-319.

  50. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "The effect of fragmentation in trading on market quality in the UK equity market," CeMMAP working papers CWP42/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers 06/15, Institute for Fiscal Studies.
    2. Bernales, Alejandro & Ladley, Daniel & Litos, Evangelos & Valenzuela, Marcela, 2021. "Dark trading and alternative execution priority rules," LSE Research Online Documents on Economics 118866, London School of Economics and Political Science, LSE Library.
    3. Oualid Bada & Alois Kneip & Dominik Liebl & Tim Mensinger & James Gualtieri & Robin C. Sickles, 2021. "A Wavelet Method for Panel Models with Jump Discontinuities in the Parameters," Papers 2109.10950, arXiv.org.
    4. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    5. Aghanya, Daniel & Agarwal, Vineet & Poshakwale, Sunil, 2020. "Market in Financial Instruments Directive (MiFID), stock price informativeness and liquidity," Journal of Banking & Finance, Elsevier, vol. 113(C).
    6. Sinem Hacıoğlu Hoke & George Kapetanios, 2021. "Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 125-150, January.
    7. Ye, Xiaoqing & Xu, Juan & Wu, Xiangjun, 2018. "Estimation of an unbalanced panel data Tobit model with interactive effects," Journal of choice modelling, Elsevier, vol. 28(C), pages 108-123.
    8. Costa, Geraldo Jr. & Trujillo-Barrera, Andres & Pennings, Joost M.E., 2018. "Concentration and Liquidity Costs in Emerging Commodity Exchanges," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(3), September.
    9. Lausen, Jens & Clapham, Benjamin & Gomber, Peter & Bender, Micha, 2022. "Drivers and effects of stock market fragmentation - Insights on SME stocks," SAFE Working Paper Series 367, Leibniz Institute for Financial Research SAFE.
    10. Anna Pomeranets & Daniel G. Weaver, 2024. "Forced consolidation," Review of Quantitative Finance and Accounting, Springer, vol. 62(2), pages 579-601, February.
    11. Paulo Pereira Silva, 2018. "Fragmentation and Market Quality: The Case of European Markets," De Economist, Springer, vol. 166(2), pages 179-206, June.
    12. Suchismita Mishra & Le Zhao, 2021. "Order Routing Decisions for a Fragmented Market: A Review," JRFM, MDPI, vol. 14(11), pages 1-32, November.
    13. Brugler, James, 2015. "Into the light: dark pool trading and intraday market quality on the primary exchange," Bank of England working papers 545, Bank of England.
    14. Daniel Chen & Darrell Duffie, 2020. "Market Fragmentation," NBER Working Papers 26828, National Bureau of Economic Research, Inc.

  51. Xiaohong Chen & David Jacho-Chávez & Oliver Linton, 2012. "Averaging of moment condition estimators," CeMMAP working papers CWP26/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Martins, Luis F. & Gabriel, Vasco J., 2014. "Linear instrumental variables model averaging estimation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 709-724.

  52. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Francisco Azuero & Jorge Armando Rodríguez, 2016. "Preservación ambiental de la Amazonia colombiana: retos para la política fiscal," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 35(Especial ), pages 281-313, January.

  53. Oliver Linton & Yoon-Jae Whang, 2012. "Testing for the stochastic dominance efficiency of a given portfolio," CeMMAP working papers CWP27/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Arvanitis, Stelios & Scaillet, Olivier & Topaloglou, Nikolas, 2020. "Spanning tests for Markowitz stochastic dominance," Journal of Econometrics, Elsevier, vol. 217(2), pages 291-311.
    2. Sree Vinutha Venkataraman & S. V. D. Nageswara Rao, 2023. "Stochastic dominance algorithms with application to mutual fund performance evaluation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 681-698, January.
    3. Stelios Arvanitis & O. Scaillet & Nikolas Topaloglou, 2020. "Spanning analysis of stock market anomalies under Prospect Stochastic Dominance," Swiss Finance Institute Research Paper Series 20-18, Swiss Finance Institute.
    4. Thierry Post & Valerio Potì, 2017. "Portfolio Analysis Using Stochastic Dominance, Relative Entropy, and Empirical Likelihood," Management Science, INFORMS, vol. 63(1), pages 153-165, January.
    5. Stelios Arvanitis & Mark Hallam & Thierry Post & Nikolas Topaloglou, 2019. "Stochastic Spanning," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 573-585, October.
    6. Mehmet Pinar & Thanasis Stengos & Nikolas Topaloglou, 2022. "Stochastic dominance spanning and augmenting the human development index with institutional quality," Annals of Operations Research, Springer, vol. 315(1), pages 341-369, August.
    7. Thierry Post & Iňaki Rodríguez Longarela, 2021. "Risk Arbitrage Opportunities for Stock Index Options," Operations Research, INFORMS, vol. 69(1), pages 100-113, January.
    8. Teng Wah Leo, 2017. "On the asymptotic distribution of (generalized) Lorenz transvariation measures," METRON, Springer;Sapienza Università di Roma, vol. 75(2), pages 195-213, August.
    9. Duc Khuong Nguyen & Nikolas Topaloglou & Thomas Walther, 2020. "Asset Classes and Portfolio Diversification: Evidence from a Stochastic Spanning Approach," Working Papers 2020-009, Department of Research, Ipag Business School.
    10. Thierry Post & Yi Fang & Miloš Kopa, 2015. "Linear Tests for Decreasing Absolute Risk Aversion Stochastic Dominance," Management Science, INFORMS, vol. 61(7), pages 1615-1629, July.
    11. Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2019. "Robust optimization of forecast combinations," International Journal of Forecasting, Elsevier, vol. 35(3), pages 910-926.
    12. Mehmet Pinar, 2015. "Measuring world governance: revisiting the institutions hypothesis," Empirical Economics, Springer, vol. 48(2), pages 747-778, March.
    13. Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2018. "Portfolio optimization based on stochastic dominance and empirical likelihood," Journal of Econometrics, Elsevier, vol. 206(1), pages 167-186.
    14. Giovanni Bernardo & Irene Brunetti & Mehmet Pinar & Thanasis Stengos, 2021. "Measuring the presence of organized crime across Italian provinces: a sensitivity analysis," European Journal of Law and Economics, Springer, vol. 51(1), pages 31-95, February.
    15. Kolokolova, Olga & Le Courtois, Olivier & Xu, Xia, 2022. "Is the index efficient? A worldwide tour with stochastic dominance," Journal of Financial Markets, Elsevier, vol. 59(PB).
    16. Pinar, Mehmet & Stengos, Thanasis & Topaloglou, Nikolas, 2020. "On the construction of a feasible range of multidimensional poverty under benchmark weight uncertainty," European Journal of Operational Research, Elsevier, vol. 281(2), pages 415-427.
    17. Stelios Arvanitis & Thierry Post & Nikolas Topaloglou, 2021. "Stochastic Bounds for Reference Sets in Portfolio Analysis," Management Science, INFORMS, vol. 67(12), pages 7737-7754, December.
    18. Charles Beach, 2023. "Quantile Tool Box Measures for Empirical Analysis and for Testing Distributional Comparisons in Direct Distribution-Free Fashion," Working Paper 1508, Economics Department, Queen's University.
    19. Stelios Arvanitis, 2015. "Saddle-Type Functionals for Continuous Processes with Applications to Tests for Stochastic Spanning," Working Papers 201509, Athens University Of Economics and Business, Department of Economics.
    20. Liesiö, Juuso & Xu, Peng & Kuosmanen, Timo, 2020. "Portfolio diversification based on stochastic dominance under incomplete probability information," European Journal of Operational Research, Elsevier, vol. 286(2), pages 755-768.
    21. Liesiö, Juuso & Kallio, Markku & Argyris, Nikolaos, 2023. "Incomplete risk-preference information in portfolio decision analysis," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1084-1098.

  54. Sujin Park & Oliver Linton, 2012. "Estimating the Quadratic Covariation Matrix for an Asynchronously Observed Continuous Time Signal Masked by Additive Noise," FMG Discussion Papers dp703, Financial Markets Group.

    Cited by:

    1. Markus Bibinger & Lars Winkelmann, 2013. "Econometrics of co-jumps in high-frequency data with noise," SFB 649 Discussion Papers SFB649DP2013-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
    3. Stefano Peluso & Fulvio Corsi & Antonietta Mira, 2015. "A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 665-697.
    4. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    5. Markus Bibinger & Per A. Mykland, 2013. "Inference for Multi-Dimensional High-Frequency Data: Equivalence of Methods, Central Limit Theorems, and an Application to Conditional Independence Testing," SFB 649 Discussion Papers SFB649DP2013-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Maria Elvira Mancino & Maria Cristina Recchioni, 2015. "Fourier Spot Volatility Estimator: Asymptotic Normality and Efficiency with Liquid and Illiquid High-Frequency Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-33, September.
    7. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.

  55. Oliver Linton & Michael Vogt, 2012. "Nonparametric estimation of a periodic sequence in the presence of a smooth trend," CeMMAP working papers CWP23/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers 06/15, Institute for Fiscal Studies.
    2. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    3. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    4. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    5. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2014. "Multivariate Variance Ratio Statistics," Cambridge Working Papers in Economics 1459, Faculty of Economics, University of Cambridge.
    6. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2014. "Multivariate variance ratio statistics," CeMMAP working papers 29/14, Institute for Fiscal Studies.
    7. Kai Yang & Peihua Qiu, 2022. "A three-step local smoothing approach for estimating the mean and covariance functions of spatio-temporal Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 49-68, February.
    8. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    9. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers CWP06/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.
    11. Liu, Jialuo & Chu, Tingjin & Zhu, Jun & Wang, Haonan, 2021. "Semiparametric method and theory for continuously indexed spatio-temporal processes," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    12. Marina Khismatullina & Michael Vogt, 2022. "Multiscale Comparison of Nonparametric Trend Curves," Papers 2209.10841, arXiv.org.

  56. Linton, Oliver & Mammen, Enno & Nielsen, Jens Perch & Van Keilegom, Ingrid, 2011. "Nonparametric regression with filtered data," LIDAM Reprints ISBA 2011008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Stephan M. Bischofberger, 2020. "In-Sample Hazard Forecasting Based on Survival Models with Operational Time," Risks, MDPI, vol. 8(1), pages 1-17, January.
    2. Sujica, Aleksandar & Van Keilegom, Ingrid, 2018. "The copula-graphic estimator in censored nonparametric location-scale regression models," Econometrics and Statistics, Elsevier, vol. 7(C), pages 89-114.
    3. Gámiz Pérez, M. Luz & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2013. "Smoothing survival densities in practice," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 368-382.
    4. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
    5. Effraimidis, Georgios, 2016. "Nonparametric Identification of a Time-Varying Frailty Model," DaCHE discussion papers 2016:6, University of Southern Denmark, Dache - Danish Centre for Health Economics.

  57. Efang Kong & Oliver Linton & Yingcun Xia, 2011. "Global Bahadur representation for nonparametric censored regression quantiles and its applications," CeMMAP working papers CWP33/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2021. "Smoothing Quantile Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 338-357, January.
    2. Eliana Christou & Michael G. Akritas, 2019. "Single index quantile regression for censored data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 655-678, December.
    3. Sungwon Lee & Joon H. Ro, 2020. "Nonparametric Tests for Conditional Quantile Independence with Duration Outcomes," Working Papers 2013, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).

  58. Degui Li & Zudi Lu & Oliver Linton, 2011. "Local Linear Fitting Under Near Epoch Dependence: Uniform consistency with Convergence Rates," Monash Econometrics and Business Statistics Working Papers 16/11, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Papers 2012.03182, arXiv.org, revised Nov 2021.
    2. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    3. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric Model Averaging of Ultra-High Dimensional Time Series," Discussion Papers 15/18, Department of Economics, University of York.
    4. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric dynamic portfolio choice with multiple conditioning variables," CeMMAP working papers CWP07/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Jiti Gao & Fei Liu & Bin peng, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 44/20, Monash University, Department of Econometrics and Business Statistics.
    7. Yang, Lixiong & Lee, Chingnun & Shie, Fu Shuen, 2014. "How close a relationship does a capital market have with other markets? A reexamination based on the equal variance test," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 198-226.
    8. James A. Duffy, 2015. "Uniform Convergence Rates over Maximal Domains in Structural Nonparametric Cointegrating Regression," Economics Papers 2015-W03, Economics Group, Nuffield College, University of Oxford.
    9. Kurisu, Daisuke, 2019. "On nonparametric inference for spatial regression models under domain expanding and infill asymptotics," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    10. Botosaru, Irene & Sasaki, Yuya, 2018. "Nonparametric heteroskedasticity in persistent panel processes: An application to earnings dynamics," Journal of Econometrics, Elsevier, vol. 203(2), pages 283-296.
    11. Wang, Hanchao & Peng, Bin & Li, Degui & Leng, Chenlei, 2021. "Nonparametric estimation of large covariance matrices with conditional sparsity," Journal of Econometrics, Elsevier, vol. 223(1), pages 53-72.
    12. Lee, Jiyon, 2015. "A semiparametric single index model with heterogeneous impacts on an unobserved variable," Journal of Econometrics, Elsevier, vol. 184(1), pages 13-36.
    13. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.

  59. Linton, Oliver & Srisuma, Sorawoot, 2010. "Semiparametric estimation of Markov decision processeswith continuous state space," LSE Research Online Documents on Economics 58187, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Otsu, Taisuke & Pesendorfer, Martin & Takahashi, Yuya, 2013. "Testing for equilibrium multiplicity in dynamic Markov games," LSE Research Online Documents on Economics 101968, London School of Economics and Political Science, LSE Library.
    2. Truquet, Lionel, 2023. "Strong mixing properties of discrete-valued time series with exogenous covariates," Stochastic Processes and their Applications, Elsevier, vol. 160(C), pages 294-317.
    3. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    4. Komarova, Tatiana & Sanches, Fábio Adriano & Silva Junior, Daniel & Srisuma, Sorawoot, 2018. "Joint analysis of the discount factor and payoff parameters in dynamic discrete choice games," LSE Research Online Documents on Economics 86858, London School of Economics and Political Science, LSE Library.
    5. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," Papers 2109.01725, arXiv.org, revised Sep 2021.
    6. Taisuke Otsu & Martin Pesendorfer, 2021. "Equilibrium multiplicity in dynamic games: testing and estimation," STICERD - Econometrics Paper Series 618, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. Pesendorfer, Martin & Takahashi, Yuya & Otsu, Taisuke, 2014. "Testing Equilibrium Multiplicity in Dynamic Games," CEPR Discussion Papers 10111, C.E.P.R. Discussion Papers.
    8. Otsu, Taisuke & Pesendorfer, Martin & Takahashi, Yuya, 2016. "Pooling data across markets in dynamic Markov games," LSE Research Online Documents on Economics 66182, London School of Economics and Political Science, LSE Library.
    9. Taisuke Otsu & Martin Pesendorfer, 2023. "Equilibrium multiplicity in dynamic games: Testing and estimation," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 26-42.
    10. Fabio A. Miessi Sanches & Daniel Silva Junior, Sorawoot Srisuma, 2014. "Ordinary Least Squares Estimation for a Dynamic Game," Working Papers, Department of Economics 2014_19, University of São Paulo (FEA-USP), revised 23 Feb 2015.
    11. Ariel Neufeld & Julian Sester & Mario v{S}iki'c, 2022. "Markov Decision Processes under Model Uncertainty," Papers 2206.06109, arXiv.org, revised Jan 2023.
    12. Ariel Neufeld & Julian Sester & Mario Šikić, 2023. "Markov decision processes under model uncertainty," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 618-665, July.
    13. Hiroyuki Kasahara & Katsumi Shimotsu, 2018. "Estimation of Discrete Choice Dynamic Programming Models," The Japanese Economic Review, Japanese Economic Association, vol. 69(1), pages 28-58, March.
    14. Buchholz, Nicholas & Shum, Matthew & Xu, Haiqing, 2021. "Semiparametric estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 223(2), pages 312-327.
    15. Armstrong, Timothy B. & Bertanha, Marinho & Hong, Han, 2014. "A fast resample method for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 179(2), pages 128-133.
    16. Otero, Karina V., 2016. "Nonparametric identification of dynamic multinomial choice games: unknown payoffs and shocks without interchangeability," MPRA Paper 86784, University Library of Munich, Germany.
    17. Sears, Louis S. & Lin Lawell, C.-Y. Cynthia & Walter, M. Todd, 2020. "Groundwater Under Open Access: A Structural Model of the Dynamic Common Pool Extraction Game," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304276, Agricultural and Applied Economics Association.

  60. Koo, Bonsoo & Linton, Oliver, 2010. "Semiparametric estimation of locally stationary diffusion models," LSE Research Online Documents on Economics 58186, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Aslanidis, Nektarios & Casas, Isabel, 2013. "Nonparametric correlation models for portfolio allocation," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2268-2283.

  61. Juan Carlos Escanciano & Stefan Hoderlein & Arthur Lewbel & Oliver Linton & Sorawoot Srisuma, 2010. "Nonparametric Euler Equation Identification and Estimation," Boston College Working Papers in Economics 757, Boston College Department of Economics, revised 15 Mar 2020.

    Cited by:

    1. Cui, Liyuan & Hong, Yongmiao & Li, Yingxing, 2021. "Solving Euler equations via two-stage nonparametric penalized splines," Journal of Econometrics, Elsevier, vol. 222(2), pages 1024-1056.
    2. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    3. Andreas Tryphonides, 2023. "Online Appendix to "Identifying Preferences when Households are Financially Constrained"," Online Appendices 21-242, Review of Economic Dynamics.
    4. Dalderop, Jeroen, 2023. "Semiparametric estimation of latent variable asset pricing models," Journal of Econometrics, Elsevier, vol. 236(1).
    5. Xiaohong Chen & Victor Chernozhukov & Sokbae (Simon) Lee & Whitney K. Newey, 2011. "Local identification of nonparametric and semiparametric models," CeMMAP working papers 17/11, Institute for Fiscal Studies.
    6. Marcel Fafchamps & Aditya Shrinivas, 2022. "Risk Pooling and Precautionary Saving in Village Economies," NBER Working Papers 30128, National Bureau of Economic Research, Inc.
    7. Giovanni Gallipoli & Brant Abbott, 2017. ""Permanent Income" Inequality," 2017 Meeting Papers 1033, Society for Economic Dynamics.
    8. Victor H. Aguiar & Nail Kashaev, 2018. "Stochastic Revealed Preferences with Measurement Error," Papers 1810.05287, arXiv.org, revised Sep 2020.
    9. Striani, Fabrizio, 2023. "Life-cycle consumption and life insurance: Empirical evidence from Italian Survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    10. Timothy M. Christensen, 2014. "Nonparametric identification of positive eigenfunctions," CeMMAP working papers CWP37/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Eshaghnia, Sadegh S. M. & Heckman, James J. & Landerso, Rasmus & Qureshi, Rafeh, 2022. "Intergenerational Transmission of Family Influence," IZA Discussion Papers 15504, Institute of Labor Economics (IZA).
    12. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness," Papers 2302.05404, arXiv.org.
    13. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," CEPR Discussion Papers 13240, C.E.P.R. Discussion Papers.
    14. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," NBER Working Papers 25134, National Bureau of Economic Research, Inc.
    15. Andreas Tryphonides, 2020. "Identifying Preferences when Households are Financially Constrained," Papers 2005.02010, arXiv.org, revised Feb 2023.

  62. Christian M. Hafner & Oliver Linton, 2010. "Efficient estimation of a multivariate multiplicative volatility model," Post-Print hal-00732539, HAL.

    Cited by:

    1. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2010. "Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets," Econometrics Working Papers Archive wp2010_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    2. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    3. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    4. Cristina Amado & Annastiina Silvennoinen & Timo Teräsvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," CREATES Research Papers 2018-14, Department of Economics and Business Economics, Aarhus University.
    5. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Yulia Kotlyarova & Marcia M Schafgans & Victoria Zinde-Walsh, 2011. "Adapting Kernel Estimation to Uncertain Smoothness," STICERD - Econometrics Paper Series 557, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    8. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    9. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    10. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2010. "The conditional autoregressive wishart model for multivariate stock market volatility," Economics Working Papers 2010-07, Christian-Albrechts-University of Kiel, Department of Economics.
    11. Khismatullina, Marina & Vogt, Michael, 2023. "Nonparametric comparison of epidemic time trends: The case of COVID-19," Journal of Econometrics, Elsevier, vol. 232(1), pages 87-108.
    12. BAUWENS, Luc & HAFNER, Christian M. & PIERRET, Diane, 2013. "Multivariate volatility modeling of electricity futures," LIDAM Reprints CORE 2526, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    13. Tommaso Proietti, 2014. "Exponential Smoothing, Long Memory and Volatility Prediction," CEIS Research Paper 319, Tor Vergata University, CEIS, revised 30 Jul 2014.
    14. Valentin Patilea & Hamdi Raïssi, 2014. "Testing Second-Order Dynamics for Autoregressive Processes in Presence of Time-Varying Variance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1099-1111, September.
    15. Quentin Giai Gianetto & Hamdi Raïssi, 2015. "Testing Instantaneous Causality in Presence of Nonconstant Unconditional Covariance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 46-53, January.
    16. Feiyu Jiang & Dong Li & Ke Zhu, 2019. "Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model," Papers 1907.04147, arXiv.org, revised Oct 2020.
    17. Hafner, Christian & Linton, Oliver & Wang, Linqi, 2022. "Dynamic Autoregressive Liquidity (DArLiQ)," LIDAM Discussion Papers LFIN 2022002, Université catholique de Louvain, Louvain Finance (LFIN).
    18. Matthieu Garcin & Clément Goulet, 2015. "Non-parameteric news impact curve: a variational approach," Documents de travail du Centre d'Economie de la Sorbonne 15086r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jul 2016.
    19. Geert Dhaene & Piet Sercu & Jianbin Wu, 2022. "Volatility spillovers: A sparse multivariate GARCH approach with an application to commodity markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(5), pages 868-887, May.
    20. Brenda López Cabrera, & Franziska Schulz,, 2013. "Volatility linkages between energy and agricultural commodity prices," SFB 649 Discussion Papers SFB649DP2013-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    21. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," LIDAM Discussion Papers CORE 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    22. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
    23. Francisco Blasques & Enzo D'Innocenzo & Siem Jan Koopman, 2021. "Common and Idiosyncratic Conditional Volatility Factors: Theory and Empirical Evidence," Tinbergen Institute Discussion Papers 21-057/III, Tinbergen Institute.
    24. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    25. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    26. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    27. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    28. Conrad, Christian & Stuermer, Karin, 2017. "On the economic determinants of optimal stock-bond portfolios: international evidence," Working Papers 0636, University of Heidelberg, Department of Economics.
    29. Matthieu Garcin & Clément Goulet, 2015. "Non-parameteric news impact curve: a variational approach," Documents de travail du Centre d'Economie de la Sorbonne 15086rr, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Feb 2017.
    30. VAN BELLEGEM, Sébastien, 2011. "Locally stationary volatility modelling," LIDAM Discussion Papers CORE 2011041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    31. Acatrinei, Marius & Gorun, Adrian & Marcu, Nicu, 2013. "A DCC-GARCH Model To Estimate the Risk to the Capital Market in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 136-148, March.
    32. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.
    33. Erhua Zhang & Xiaojun Song & Jilin Wu, 2022. "A non‐parametric test for multi‐variate trend functions," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 856-871, November.
    34. Jiang, Feiyu & Li, Dong & Zhu, Ke, 2021. "Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model," Journal of Econometrics, Elsevier, vol. 224(2), pages 306-329.
    35. Annastiina Silvennoinen & Timo Teräsvirta, 2017. "Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model," CREATES Research Papers 2017-28, Department of Economics and Business Economics, Aarhus University.
    36. Yuanyuan Zhang & Rong Liu & Qin Shao & Lijian Yang, 2020. "Two‐Step Estimation for Time Varying Arch Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 551-570, July.

  63. Atak, Alev & Linton, Oliver B. & Xiao, Zhijie, 2010. "A Semiparametric Panel Model for Unbalanced Data with Application to Climate Change in the United Kingdom," MPRA Paper 22079, University Library of Munich, Germany.

    Cited by:

    1. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.
    2. Javier Hidalgo & Jungyoon Lee, 2014. "A Cusum Test of Common Trends in Large Heterogeneous Panels," STICERD - Econometrics Paper Series 576, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "Semiparametric trending panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 171(1), pages 71-85.
    4. Souza, Wallace Patrick Santos de Farias & Annegues, Ana Claudia & Rodrigues de Oliveira, Victor, 2017. "Thoughts on the inequality of opportunities: new evidence," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), April.
    5. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers 02/13, Institute for Fiscal Studies.
    6. Zhu, Xiaoqian & Xie, Yongjia & Li, Jianping & Wu, Dengsheng, 2015. "Change point detection for subprime crisis in American banking: From the perspective of risk dependence," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 18-28.
    7. Oliver Linton & Michael Vogt, 2012. "Nonparametric estimation of a periodic sequence in the presence of a smooth trend," CeMMAP working papers CWP23/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Chen, Zhihong & Xia, Huizhu, 2020. "Trend instrumental variable regression with an application to the US New Keynesian Phillips Curve," Economic Modelling, Elsevier, vol. 93(C), pages 595-604.
    9. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Yonghui Zhang & Liangjun Su & Peter C. B. Phillips, 2012. "Testing for common trends in semi‐parametric panel data models with fixed effects," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 56-100, February.
    11. Chen, Bin & Huang, Liquan, 2018. "Nonparametric testing for smooth structural changes in panel data models," Journal of Econometrics, Elsevier, vol. 202(2), pages 245-267.
    12. Xu, Ke-Li, 2016. "Multivariate trend function testing with mixed stationary and integrated disturbances," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 38-57.
    13. Jia Chen & Degui Li & Jiti Gao, 2013. "Non- and Semi-Parametric Panel Data Models: A Selective Review," Monash Econometrics and Business Statistics Working Papers 18/13, Monash University, Department of Econometrics and Business Statistics.
    14. Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.
    15. Marina Khismatullina & Michael Vogt, 2022. "Multiscale Comparison of Nonparametric Trend Curves," Papers 2209.10841, arXiv.org.

  64. Oliver Linton & Kyungchul Song & Yoon-Jae Whang, 2009. "An Improved Bootstrap Test of Stochastic Dominance," Cowles Foundation Discussion Papers 1713, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers CWP24/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & Maasoumi, Esfandiar & McAleer, Michael & Pérez-Amaral, Teodosio, 2019. "Choosing expected shortfall over VaR in Basel III using stochastic dominance," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 95-113.
    3. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2021. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP16/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    5. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers 24/12, Institute for Fiscal Studies.
    6. Linton, Oliver & Seo, Myung Hwan & Whang, Yoon-Jae, 2023. "Testing stochastic dominance with many conditioning variables," Journal of Econometrics, Elsevier, vol. 235(2), pages 507-527.
    7. Arvanitis, Stelios & Post, Thierry & Potì, Valerio & Karabati, Selcuk, 2021. "Nonparametric tests for Optimal Predictive Ability," International Journal of Forecasting, Elsevier, vol. 37(2), pages 881-898.
    8. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers CWP51/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers CWP09/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Oliver Linton & Katja Smetanina, 2015. "Mean Ratio Statistic for measuring predictability," CeMMAP working papers 08/15, Institute for Fiscal Studies.
    11. Donald W.K. Andrews & Xiaoxia Shi, 2010. "Inference Based on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1761R2, Cowles Foundation for Research in Economics, Yale University, revised May 2012.
    12. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
    13. Bogomolov, Marina & Davidov, Ori, 2019. "Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 20-27.
    14. Yu Zhu, 2020. "Inference in nonparametric/semiparametric moment equality models with shape restrictions," Quantitative Economics, Econometric Society, vol. 11(2), pages 609-636, May.
    15. Kai-Yin Woo & Chulin Mai & Michael McAleer & Wing-Keung Wong, 2020. "Review on Efficiency and Anomalies in Stock Markets," Economies, MDPI, vol. 8(1), pages 1-51, March.
    16. Chang, Chia-Lin & Jiménez-Martín, Juan-Ángel & Maasoumi, Esfandiar & Pérez-Amaral, Teodosio, 2015. "A stochastic dominance approach to financial risk management strategies," Journal of Econometrics, Elsevier, vol. 187(2), pages 472-485.
    17. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Identification of Panel Data Models with Endogenous Censoring," MPRA Paper 30373, University Library of Munich, Germany.
    18. Andrey Lizyayev, 2012. "Stochastic dominance efficiency analysis of diversified portfolios: classification, comparison and refinements," Annals of Operations Research, Springer, vol. 196(1), pages 391-410, July.
    19. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    20. Jesus Gonzalo & Jose Olmo, 2014. "Conditional Stochastic Dominance Tests In Dynamic Settings," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 819-838, August.
    21. Brendan K. Beare & Juwon Seo, 2022. "Stochastic arbitrage with market index options," Papers 2207.00949, arXiv.org, revised Jul 2022.
    22. Topaloglou, Nikolas & Tsionas, Mike G., 2020. "Stochastic dominance tests," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    23. Lok, Thomas M. & Tabri, Rami V., 2021. "An improved bootstrap test for restricted stochastic dominance," Journal of Econometrics, Elsevier, vol. 224(2), pages 371-393.
    24. Sokbae Lee & Yoon-Jae Whang, 2009. "Nonparametric Tests of Conditional Treatment Effects," Cowles Foundation Discussion Papers 1740, Cowles Foundation for Research in Economics, Yale University.
    25. Lee, K. & Linton, O. & Whang, Y-J., 2020. "Testing for Time Stochastic Dominance," Cambridge Working Papers in Economics 20121, Faculty of Economics, University of Cambridge.
    26. Chrisopher J. Bennett & Brennan S. Thompson, 2012. "Moving the Goalposts: Subjective Performance Benchmarks and the Aumann-Serrano Measure of Riskiness," Working Papers 057, Ryerson University, Department of Economics, revised Oct 2014.
    27. Donald W.K. Andrews & Xu Cheng & Patrik Guggenberger, 2011. "Generic Results for Establishing the Asymptotic Size of Confidence Sets and Tests," Cowles Foundation Discussion Papers 1813, Cowles Foundation for Research in Economics, Yale University.
    28. Firpo, Sergio & Galvao, Antonio F. & Parker, Thomas, 2023. "Uniform inference for value functions," Journal of Econometrics, Elsevier, vol. 235(2), pages 1680-1699.
    29. Le-Yu Chen & Jerzy Szroeter, 2012. "Testing multiple inequality hypotheses: a smoothed indicator approach," CeMMAP working papers 16/12, Institute for Fiscal Studies.
    30. Parker, Thomas, 2019. "Asymptotic inference for the constrained quantile regression process," Journal of Econometrics, Elsevier, vol. 213(1), pages 174-189.
    31. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2013. "A nonparametric test of a strong leverage hypothesis," CeMMAP working papers 28/13, Institute for Fiscal Studies.
    32. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers CWP53/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    33. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.
    34. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2017. "Bayesian Assessment of Lorenz and Stochastic Dominance," Department of Economics - Working Papers Series 2029, The University of Melbourne.
    35. Daniel Hedblom & Brent Hickman & John List, 2019. "Toward an Understanding of Corporate Social Responsibility: Theory and Field Experimental Evidence," Natural Field Experiments 00675, The Field Experiments Website.
    36. Hongyi Jiang & Zhenting Sun & Shiyun Hu, 2023. "A Nonparametric Test of $m$th-degree Inverse Stochastic Dominance," Papers 2306.12271, arXiv.org, revised Jul 2023.
    37. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    38. Qihui Chen & Zheng Fang, 2019. "Inference on Functionals under First Order Degeneracy," Papers 1901.04861, arXiv.org.
    39. Zeng-Hua Lu, 2020. "Bahadur intercept with applications to one-sided testing," Statistical Papers, Springer, vol. 61(2), pages 645-658, April.
    40. Arkadiusz Szydlowski, 2015. "Endogenous Censoring in the Mixed Proportional Hazard Model with an Application to Optimal Unemployment Insurance," Discussion Papers in Economics 15/06, Division of Economics, School of Business, University of Leicester.
    41. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers 53/13, Institute for Fiscal Studies.
    42. Marat Molyboga & Seungho Baek & John F. O. Bilson, 2017. "Assessing hedge fund performance with institutional constraints: evidence from CTA funds," Journal of Asset Management, Palgrave Macmillan, vol. 18(7), pages 547-565, December.
    43. Rahul Deb & Ludovic Renou, 2022. "Which wage distributions are consistent with statistical discrimination?," Working Papers tecipa-736, University of Toronto, Department of Economics.
    44. Sergio Firpo & Antonio F. Galvao & Martyna Kobus & Thomas Parker & Pedro Rosa-Dias, 2020. "Loss aversion and the welfare ranking of policy interventions," Papers 2004.08468, arXiv.org, revised Sep 2023.
    45. Donald W.K. Andrews & Xiaoxia Shi, 2015. "Inference Based on Many Conditional Moment Inequalities," Cowles Foundation Discussion Papers 2010, Cowles Foundation for Research in Economics, Yale University.
    46. Jin, Sainan & Corradi, Valentina & Swanson, Norman R., 2017. "Robust Forecast Comparison," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1306-1351, December.
    47. Nathan Canen & Kyungchul Song, 2019. "Counterfactual Analysis under Partial Identification Using Locally Robust Refinement," Papers 1906.00003, arXiv.org, revised Jan 2021.
    48. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    49. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    50. Arvanitis, Stelios & Topaloglou, Nikolas, 2017. "Testing for prospect and Markowitz stochastic dominance efficiency," Journal of Econometrics, Elsevier, vol. 198(2), pages 253-270.
    51. Ai, Chunrong & Linton, Oliver & Zhang, Zheng, 2022. "Estimation and inference for the counterfactual distribution and quantile functions in continuous treatment models," Journal of Econometrics, Elsevier, vol. 228(1), pages 39-61.
    52. D'Haultfoeuille, Xavier & Gaillac, Christophe & Maurel, Arnaud, 2021. "Rationalizing Rational Expectations: Characterizations and Tests," TSE Working Papers 21-1211, Toulouse School of Economics (TSE).
    53. Chung, D. & Linton, O. & Whang Y-J., 2021. "Consistent Testing for an Implication of Supermodular Dominance," Cambridge Working Papers in Economics 2134, Faculty of Economics, University of Cambridge.
    54. Zeng-Hua Lu & Alec Zuo, 2017. "Child disability, welfare payments, marital status and mothers’ labor supply: Evidence from Australia," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1339769-133, January.
    55. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.
    56. Abdul Rashid & Saba Kausar, 2019. "Testing the Monthly Calendar Anomaly of Stock Returns in Pakistan: A Stochastic Dominance Approach," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 58(1), pages 83-104.
    57. Oliver Linton & Katja Smetanina, 2015. "Mean Ratio Statistic for measuring predictability," CeMMAP working papers CWP08/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    58. Miguel A. Delgado & Juan Carlos Escanciano, 2013. "Conditional Stochastic Dominance Testing," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 16-28, January.
    59. Linton, Oliver & Smetanina, Ekaterina, 2016. "Testing the martingale hypothesis for gross returns," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 664-689.
    60. Yu‐Chin Hsu & Shu Shen, 2021. "Testing monotonicity of conditional treatment effects under regression discontinuity designs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 346-366, April.
    61. Donald, Stephen G. & Hsu, Yu-Chin, 2014. "Estimation and inference for distribution functions and quantile functions in treatment effect models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 383-397.
    62. Hongyi Jiang & Zhenting Sun, 2023. "Testing Partial Instrument Monotonicity," Papers 2308.08390, arXiv.org, revised Aug 2023.
    63. Mohamed Khaled & Paul Makdissi & Myra Yazbeck, 2016. "Income-Related Health Transfers Principles and Orderings of Joint Distributions of Income and Health," Working Papers 160009, Canadian Centre for Health Economics.
    64. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.
    65. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    66. Fan, Yanqin & Park, Sang Soo, 2012. "Confidence intervals for the quantile of treatment effects in randomized experiments," Journal of Econometrics, Elsevier, vol. 167(2), pages 330-344.
    67. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    68. P. C. Álvarez-Esteban & E. del Barrio & J. A. Cuesta-Albertos & C. Matrán, 2016. "A contamination model for the stochastic order," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(4), pages 751-774, December.
    69. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    70. Sung Jae Jun & Yoonseok Lee & Youngki Shin, 2016. "Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 302-311, April.
    71. Lee, Kyungho & Linton, Oliver & Whang, Yoon-Jae, 2023. "Testing for time stochastic dominance," Journal of Econometrics, Elsevier, vol. 235(2), pages 352-371.
    72. Brendan K. Beare & Jackson D. Clarke, 2022. "Modified Wilcoxon-Mann-Whitney tests of stochastic dominance," Papers 2210.08892, arXiv.org.
    73. Ng, Pin & Wong, Wing-Keung & Xiao, Zhijie, 2017. "Stochastic dominance via quantile regression with applications to investigate arbitrage opportunity and market efficiency," European Journal of Operational Research, Elsevier, vol. 261(2), pages 666-678.
    74. Chen, Qihui & Fang, Zheng, 2019. "Inference on functionals under first order degeneracy," Journal of Econometrics, Elsevier, vol. 210(2), pages 459-481.
    75. David Lander & David Gunawan & William E. Griffiths & Duangkamon Chotikapanich, 2016. "Bayesian Assessment of Lorenz and Stochastic Dominance Using a Mixture of Gamma Densities," Department of Economics - Working Papers Series 2023, The University of Melbourne.
    76. Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2021. "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data," Papers 2106.04237, arXiv.org, revised Aug 2022.
    77. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    78. Garry F. Barrett & Stephen G. Donald & Yu-Chin Hsu, 2015. "Consistent Tests for Poverty Dominance Relations," IEAS Working Paper : academic research 15-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    79. Fan, Yanqin & Park, Sang Soo, 2014. "Nonparametric inference for counterfactual means: Bias-correction, confidence sets, and weak IV," Journal of Econometrics, Elsevier, vol. 178(P1), pages 45-56.
    80. Valentina Corradi & Sainan Jin & Norman R. Swanson, 2023. "Robust forecast superiority testing with an application to assessing pools of expert forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 596-622, June.
    81. Toru Kitagawa & Aleksey Tetenov, 2017. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers 24/17, Institute for Fiscal Studies.
    82. Ram N. Acharya & Rajan Ghimire & Apar GC & Don Blayney, 2019. "Effect of Cover Crop on Farm Profitability and Risk in the Southern High Plains," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    83. Amparo Ba'illo & Javier C'arcamo & Carlos Mora-Corral, 2024. "Tests for almost stochastic dominance," Papers 2403.15258, arXiv.org.
    84. Chuang, O-Chia & Kuan, Chung-Ming & Tzeng, Larry Y., 2017. "Testing for central dominance: Method and application," Journal of Econometrics, Elsevier, vol. 196(2), pages 368-378.
    85. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers 09/14, Institute for Fiscal Studies.
    86. Zhenting Sun & Kaspar Wuthrich, 2022. "Pairwise Valid Instruments," Papers 2203.08050, arXiv.org, revised Jan 2024.
    87. Kolokolova, Olga & Le Courtois, Olivier & Xu, Xia, 2022. "Is the index efficient? A worldwide tour with stochastic dominance," Journal of Financial Markets, Elsevier, vol. 59(PB).
    88. Kyungchul Song, 2009. "Testing Predictive Ability and Power Robustification," PIER Working Paper Archive 09-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    89. Gibson, Grant & Clair, Luc, 2019. "O brother how art thou: Propensity to report self-assessed unmet need," Social Science & Medicine, Elsevier, vol. 243(C).
    90. Seo, Juwon, 2018. "Tests of stochastic monotonicity with improved power," Journal of Econometrics, Elsevier, vol. 207(1), pages 53-70.

  65. Oliver Linton & Søren Feodor Nielsen & Jens Perch Nielsen, 2009. "Nonparametric Regression with a Latent Time Series," STICERD - Econometrics Paper Series 538, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    Cited by:

    1. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Buch-Kromann, Tine & Guillén, Montserrat & Linton, Oliver & Nielsen, Jens Perch, 2011. "Multivariate density estimation using dimension reducing information and tail flattening transformations," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 99-110, January.
    3. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers 02/13, Institute for Fiscal Studies.
    4. Dursun Aydın & Ersin Yılmaz, 2021. "Semiparametric modeling of the right-censored time-series based on different censorship solution techniques," Empirical Economics, Springer, vol. 61(4), pages 2143-2172, October.
    5. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  66. Woocheol Kim & Oliver Linton, 2009. "Estimation Of A Semiparametricigarch(1,1) Model," STICERD - Econometrics Paper Series 539, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    Cited by:

    1. Till Strohsal & Enzo Weber, 2014. "Mean-variance cointegration and the expectations hypothesis," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 1983-1997, November.

  67. Chen, Xiaohong & Linton, Oliver & Jacho-Chávez, David T., 2009. "An alternative way of computing efficient instrumental variable estimators," LSE Research Online Documents on Economics 58016, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Neil Shephard & Kevin Sheppard & Robert F. Engle, 2008. "Fitting vast dimensional time-varying covariance models," Economics Series Working Papers 403, University of Oxford, Department of Economics.
    2. Yves Dominicy & Hiroaki Ogata & David Veredas, 2013. "Inference for vast dimensional elliptical distributions," Computational Statistics, Springer, vol. 28(4), pages 1853-1880, August.
    3. Carrasco, Marine, 2012. "A regularization approach to the many instruments problem," Journal of Econometrics, Elsevier, vol. 170(2), pages 383-398.
    4. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.
    5. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
    6. Sla{dj}ana Babi'c & Christophe Ley & Lorenzo Ricci & David Veredas, 2020. "TailCoR," Papers 2011.14817, arXiv.org.
    7. Francis DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 15-027, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 Aug 2015.

  68. Efang Kong & Oliver Linton & Yingcun Xia, 2009. "Uniform Bahadur Representation for LocalPolynomial Estimates of M-Regressionand Its Application to The Additive Model," STICERD - Econometrics Paper Series 535, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    Cited by:

    1. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    2. Graciela Boente & Alejandra Martínez, 2017. "Marginal integration M-estimators for additive models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 231-260, June.
    3. Marc Hallin & Zudi Lu & Davy Paindaveine & Miroslav Siman, 2012. "Local Constant and Local Bilinear Multiple-Output Quantile Regression," Working Papers ECARES ECARES 2012-003, ULB -- Universite Libre de Bruxelles.
    4. Enno Mammen & Christoph Rothe & Melanie Schienle, 2011. "Semiparametric Estimation with Generated Covariates," SFB 649 Discussion Papers SFB649DP2011-064, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Song, Song & Ritov, Ya’acov & Härdle, Wolfgang K., 2012. "Bootstrap confidence bands and partial linear quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 244-262.
    6. Shin Kanaya, 2015. "Uniform Convergence Rates of Kernel-Based Nonparametric Estimators for Continuous Time Diffusion Processes: A Damping Function Approach," CREATES Research Papers 2015-50, Department of Economics and Business Economics, Aarhus University.
    7. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.
    8. Härdle, Wolfgang Karl & Ritov, Ya’acov & Wang, Weining, 2015. "Tie the straps: Uniform bootstrap confidence bands for semiparametric additive models," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 129-145.
    9. Fan, Yanqin & Guerre, Emmanuel & Zhu, Dongming, 2017. "Partial identification of functionals of the joint distribution of “potential outcomes”," Journal of Econometrics, Elsevier, vol. 197(1), pages 42-59.
    10. Rothe, Christoph & Firpo, Sergio, 2013. "Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions," IZA Discussion Papers 7564, Institute of Labor Economics (IZA).
    11. Bravo, Francesco & Chu, Ba & Jacho-Chavez, David, 2013. "Semiparametric estimation of moment condition models with weakly dependent data," MPRA Paper 79686, University Library of Munich, Germany, revised 2016.
    12. Zongwu Cai & Xiyuan Liu, 2020. "A Functional-Coefficient VAR Model for Dynamic Quantiles with Constructing Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202017, University of Kansas, Department of Economics, revised Oct 2020.
    13. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    14. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    15. Debopam Bhattacharya & Pascaline Dupas & Shin Kanaya, 2013. "Estimating the Impact of Means-tested Subsidies under Treatment Externalities with Application to Anti-Malarial Bednets," Economics Series Working Papers 646, University of Oxford, Department of Economics.
    16. Härdle, Wolfgang K. & Song, Song, 2010. "Confidence Bands In Quantile Regression," Econometric Theory, Cambridge University Press, vol. 26(4), pages 1180-1200, August.
    17. Jia-Young Michael Fu & Joel L. Horowitz & Matthias Parey, 2015. "Testing exogeneity in nonparametric instrumental variables identified by conditional quantile restrictions," CeMMAP working papers CWP68/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Pedro H. C. Sant'Anna & Qi Xu, 2023. "Difference-in-Differences with Compositional Changes," Papers 2304.13925, arXiv.org.
    19. Sarnetzki, Florian & Dzemski, Andreas, 2014. "Overidentification test in a nonparametric treatment model with unobserved heterogeneity," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100620, Verein für Socialpolitik / German Economic Association.
    20. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    21. Wolfgang Karl Härdle & Ya'acov Ritov & Weining Wang, 2013. "Tie the straps: uniform bootstrap confidence bands for bounded influence curve estimators," SFB 649 Discussion Papers SFB649DP2013-047, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    22. Pavel Boček & Miroslav Šiman, 2017. "On weighted and locally polynomial directional quantile regression," Computational Statistics, Springer, vol. 32(3), pages 929-946, September.
    23. Sadikoglu, Serhan, 2019. "Essays in econometric theory," Other publications TiSEM 99d83644-f9dc-49e3-a4e1-5, Tilburg University, School of Economics and Management.
    24. Gerard, François & Rokkanen, Miikka & Rothe, Christoph, 2016. "Identification and Inference in Regression Discontinuity Designs with a Manipulated Running Variable," CEPR Discussion Papers 11048, C.E.P.R. Discussion Papers.
    25. Shih-Kang Chao & Katharina Proksch & Holger Dette & Wolfgang Härdle, 2014. "Confidence Corridors for Multivariate Generalized Quantile Regression," SFB 649 Discussion Papers SFB649DP2014-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    26. Juan Carlos Escanciano, 2020. "Uniform Rates for Kernel Estimators of Weakly Dependent Data," Papers 2005.09951, arXiv.org.
    27. Cizek, Pavel & Sadikoglu, Serhan, 2022. "Nonseparable Panel Models with Index Structure and Correlated Random Effects," Discussion Paper 2022-009, Tilburg University, Center for Economic Research.
    28. Mammen, Enno & Van Keilegom, Ingrid & Yu, Kyusang, 2013. "Expansion for Moments of Regression Quantiles with Applications to Nonparametric Testing," LIDAM Discussion Papers ISBA 2013027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    29. Laurent Davezies & Xavier D'Haultfoeuille & Louise Laage, 2021. "Identification and Estimation of Average Marginal Effects in Fixed Effects Logit Models," Papers 2105.00879, arXiv.org, revised Oct 2022.
    30. Yingying DONG & Ying-Ying LEE & Michael GOU, 2019. "Regression Discontinuity Designs with a Continuous Treatment," Discussion papers 19058, Research Institute of Economy, Trade and Industry (RIETI).
    31. Eliana Christou & Annabel Settle & Andreas Artemiou, 2021. "Nonlinear dimension reduction for conditional quantiles," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 937-956, December.
    32. Li, Jialiang & Zhang, Wenyang & Kong, Efang, 2018. "Factor models for asset returns based on transformed factors," Journal of Econometrics, Elsevier, vol. 207(2), pages 432-448.
    33. Christou, Eliana & Akritas, Michael G., 2016. "Single index quantile regression for heteroscedastic data," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 169-182.
    34. Cattaneo, Matias D. & Farrell, Max H., 2013. "Optimal convergence rates, Bahadur representation, and asymptotic normality of partitioning estimators," Journal of Econometrics, Elsevier, vol. 174(2), pages 127-143.
    35. Chiang, Harold D. & Sasaki, Yuya, 2019. "Causal inference by quantile regression kink designs," Journal of Econometrics, Elsevier, vol. 210(2), pages 405-433.
    36. Haitian Xie, 2021. "Uniform Convergence Results for the Local Linear Regression Estimation of the Conditional Distribution," Papers 2112.08546, arXiv.org, revised Jun 2023.
    37. Efang Kong & Oliver Linton & Yingcun Xia, 2011. "Global Bahadur representation for nonparametric censored regression quantiles and its applications," CeMMAP working papers CWP33/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    38. Laura Liu & Alexandre Poirier & Ji-Liang Shiu, 2021. "Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models," Papers 2105.12891, arXiv.org, revised Dec 2023.
    39. Wolfgang Karl Härdle & Ya’acov Ritov & Song Song, 2010. "Partial Linear Quantile Regression and Bootstrap Confidence Bands," SFB 649 Discussion Papers SFB649DP2010-002, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    40. Daniel Hlubinka & Lukáš Kotík & Miroslav Šiman, 2022. "Multivariate quantiles with both overall and directional probability interpretation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1586-1604, December.
    41. Bonsoo Koo & Oliver Linton, 2013. "Let's get LADE: robust estimation of semiparametric multiplicative volatility models," CeMMAP working papers 11/13, Institute for Fiscal Studies.
    42. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2020. "Inferences for Partially Conditional Quantile Treatment Effect Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202005, University of Kansas, Department of Economics, revised Feb 2020.
    43. Noh, Hohsuk & El Ghouch, Anouar & Van Keilegom, Ingrid, 2011. "Quality of fit measures in the framework of quantile regression," LIDAM Discussion Papers ISBA 2011025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    44. Emmanuel Guerre & Camille Sabbah, 2009. "Uniform Bias Study and Bahadur Representation for Local Polynomial Estimators of the Conditional Quantile Function," Working Papers 648, Queen Mary University of London, School of Economics and Finance.

  69. Iglesias, Emma M. & Linton, Oliver, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," UC3M Working papers. Economics we094726, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Iglesias, Emma M., 2015. "Value at Risk of the main stock market indexes in the European Union (2000–2012)," Journal of Policy Modeling, Elsevier, vol. 37(1), pages 1-13.
    2. Emma M. Iglesias, 2012. "An analysis of extreme movements of exchange rates of the main currencies traded in the Foreign Exchange market," Applied Economics, Taylor & Francis Journals, vol. 44(35), pages 4631-4637, December.
    3. Iglesias, Emma M., 2015. "Value at Risk and expected shortfall of firms in the main European Union stock market indexes: A detailed analysis by economic sectors and geographical situation," Economic Modelling, Elsevier, vol. 50(C), pages 1-8.
    4. Ngai Chan & Liang Peng & Rongmao Zhang, 2012. "Interval estimation of the tail index of a GARCH(1,1) model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 546-565, September.
    5. Degiannakis, Stavros & Floros, Christos & Livada, Alexandra, 2012. "Evaluating Value-at-Risk Models before and after the Financial Crisis of 2008: International Evidence," MPRA Paper 80463, University Library of Munich, Germany.
    6. Beran, Jan & Schell, Dieter, 2012. "On robust tail index estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3430-3443.

  70. Gordon Anderson & Oliver Linton & Yoon-Jae Wang, 2009. "Non Parametric Estimation of a Polarization Measure," Working Papers tecipa-363, University of Toronto, Department of Economics.

    Cited by:

    1. Sokbae Lee & Yoon-Jae Whang, 2009. "Nonparametric Tests of Conditional Treatment Effects," Cowles Foundation Discussion Papers 1740, Cowles Foundation for Research in Economics, Yale University.
    2. Gordon Anderson, 2010. "Polarization Of The Poor: Multivariate Relative Poverty Measurement Sans Frontiers," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(1), pages 84-101, March.
    3. Gordon Anderson & Teng Wah Leo & Oliver Linton, 2010. "Making Inferences About Rich Country - Poor Country Convergence: The Polarization Trapezoid and Overlap measures," Working Papers tecipa-387, University of Toronto, Department of Economics.
    4. Gordon Anderson & Kinda Hachem, 2009. "Institutions and Economic Outcomes: A Dominance Based Analysis of Causality and Multivariate Welfare With Discrete and Continuous Variables," Working Papers tecipa-378, University of Toronto, Department of Economics.
    5. Amal Helu & Hani Samawi & Robert Vogel, 2011. "Nonparametric overlap coefficient estimation using ranked set sampling," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 385-397.
    6. Gordon Anderson & Oliver Linton & Teng Leo, 2012. "A polarization-cohesion perspective on cross-country convergence," Journal of Economic Growth, Springer, vol. 17(1), pages 49-69, March.
    7. Maria Grazia Pittau & Roberto Zelli & Paul A. Johnson, 2010. "Mixture Models, Convergence Clubs, And Polarization," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(1), pages 102-122, March.
    8. Hani M. Samawi & Amal Helu & Robert Vogel, 2011. "A nonparametric test of symmetry based on the overlapping coefficient," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 885-898, February.

  71. Yingcun Xia & Wolfgang Härdle & Oliver Linton, 2009. "Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator," SFB 649 Discussion Papers SFB649DP2009-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

    Cited by:

    1. Michal Grajek & Lars-Hendrik Röller, 2009. "Regulation and investment in network industries: Evidence from European telecoms," ESMT Research Working Papers ESMT-09-004, ESMT European School of Management and Technology.
    2. Maria Grith & Wolfgang Härdle & Juhyun Park, 2009. "Shape invariant modelling pricing kernels and risk aversion," SFB 649 Discussion Papers SFB649DP2009-041, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Roland Strausz, 2010. "The Political Economy of Regulatory Risk," CESifo Working Paper Series 2953, CESifo.
    4. Chuan Goh, 2009. "Bootstrap-based Bandwidth Selection for Semiparametric Generalized Regression Estimators," Working Papers tecipa-375, University of Toronto, Department of Economics.
    5. Barbara Choroś & Wolfgang Härdle & Ostap Okhrin, 2009. "CDO and HAC," SFB 649 Discussion Papers SFB649DP2009-038, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

  72. Sokbae (Simon) Lee & Oliver Linton & Yoon-Jae Whang, 2008. "Testing for stochastic monotonicity," CeMMAP working papers CWP21/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    2. Ismael Mourifie & Marc Henry & Romuald Meango, 2017. "Sharp bounds and testability of a Roy model of STEM major choices," Papers 1709.09284, arXiv.org, revised Nov 2019.
    3. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    4. Takashi Kamihigashi & John Stachurski, 2011. "Existence, Stability and Computation of Stationary Distributions: An Extension of the Hopenhayn-Prescott Theorem," Discussion Paper Series DP2011-32, Research Institute for Economics & Business Administration, Kobe University.
    5. Misha Beek & Hennie Daniels, 2014. "A Non-parametric Test for Partial Monotonicity in Multiple Regression," Computational Economics, Springer;Society for Computational Economics, vol. 44(1), pages 87-100, June.
    6. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    7. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2014. "Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting," Working Papers 201404, University of California at Riverside, Department of Economics.
    8. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    9. Markus Jantti & Stephen P. Jenkins, 2014. "Income Mobility," Working Papers 319, ECINEQ, Society for the Study of Economic Inequality.
    10. Takashi Kamihigashi & John Stachurski, 2011. "Stability of Stationary Distributions in Monotone Economies," ANU Working Papers in Economics and Econometrics 2011-561, Australian National University, College of Business and Economics, School of Economics.
    11. Salim Bouzebda & Amel Nezzal & Tarek Zari, 2022. "Uniform Consistency for Functional Conditional U -Statistics Using Delta-Sequences," Mathematics, MDPI, vol. 11(1), pages 1-39, December.
    12. Daniel Wilhelm, 2019. "Testing for the presence of measurement error," CeMMAP working papers CWP48/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Gutknecht, Daniel, 2016. "Testing for monotonicity under endogeneity," Journal of Econometrics, Elsevier, vol. 190(1), pages 100-114.
    14. Le-Yu Chen & Jerzy Szroeter, 2009. "Hypothesis testing of multiple inequalities: the method of constraint chaining," CeMMAP working papers CWP13/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Salim Bouzebda & Thouria El-hadjali & Anouar Abdeldjaoued Ferfache, 2023. "Uniform in Bandwidth Consistency of Conditional U-statistics Adaptive to Intrinsic Dimension in Presence of Censored Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1548-1606, August.
    16. Daouia, Abdelaati & Noh, Hohsuk & Park, Byeong U., 2013. "Data envelope fitting with constrained polynomial splines," TSE Working Papers 13-449, Toulouse School of Economics (TSE).
    17. Denis Chetverikov & Daniel Wilhelm, 2017. "Nonparametric instrumental variable estimation under monotonicity," CeMMAP working papers CWP14/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    19. Chetverikov, Denis & Wilhelm, Daniel & Kim, Dongwoo, 2021. "An Adaptive Test Of Stochastic Monotonicity," Econometric Theory, Cambridge University Press, vol. 37(3), pages 495-536, June.
    20. Arden Finn & Murray Leibbrandt & Vimal Ranchhod, 2016. "Patterns of persistence: Intergenerational mobility and education in South Africa," SALDRU Working Papers 175, Southern Africa Labour and Development Research Unit, University of Cape Town.
    21. Aradillas-López, Andrés & Gandhi, Amit & Quint, Daniel, 2016. "A simple test for moment inequality models with an application to English auctions," Journal of Econometrics, Elsevier, vol. 194(1), pages 96-115.
    22. Delgado, Miguel A. & Escanciano, Juan Carlos, 2012. "Distribution-free tests of stochastic monotonicity," Journal of Econometrics, Elsevier, vol. 170(1), pages 68-75.
    23. Feng, Long & Lan, Wei & Liu, Binghui & Ma, Yanyuan, 2022. "High-dimensional test for alpha in linear factor pricing models with sparse alternatives," Journal of Econometrics, Elsevier, vol. 229(1), pages 152-175.
    24. José Romeo & Nelson Tanaka & Antonio Pedroso-de-Lima & Victor Salinas-Torres, 2013. "Large sample properties for a class of copulas in bivariate survival analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 997-1015, November.
    25. Maribel Jiménez, 2016. "Movilidad Intergeneracional del Ingreso en Argentina. Un Análisis de sus Cambios Temporales desde el Enfoque de Igualdad de Oportunidades," CEDLAS, Working Papers 0203, CEDLAS, Universidad Nacional de La Plata.
    26. Ibarra-Ramírez Raúl, 2011. "Stocks, Bonds and the Investment Horizon: A Spatial Dominance Approach," Working Papers 2011-03, Banco de México.
    27. Vladimir Vladimirovich Vinogradov & Richard Bruce Paris, 2021. "On two extensions of the canonical Feller–Spitzer distribution," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-25, December.
    28. Takashi Kamihigashi & John Stachurski, 2014. "Partial Stochastic Dominance," Discussion Paper Series DP2014-23, Research Institute for Economics & Business Administration, Kobe University.
    29. Armstrong, Timothy B. & Chan, Hock Peng, 2016. "Multiscale adaptive inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 194(1), pages 24-43.
    30. Daniel Gutknecht, 2013. "Testing for Monotonicity under Endogeneity An Application to the Reservation Wage Function," Economics Series Working Papers 673, University of Oxford, Department of Economics.
    31. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    32. Valentino Dardanoni & Mario Fiorini & Antonio Forcina, 2008. "Stochastic Monotonicity in Intergenerational Mobility Tables," Working Paper Series 156, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    33. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    34. Christopher A. Hennessy & Ilya A. Strebulaev, 2020. "Beyond Random Assignment: Credible Inference and Extrapolation in Dynamic Economies," Journal of Finance, American Finance Association, vol. 75(2), pages 825-866, April.
    35. Berghaus, Betina & Bücher, Axel, 2014. "Nonparametric tests for tail monotonicity," Journal of Econometrics, Elsevier, vol. 180(2), pages 117-126.
    36. Salim Bouzebda & Inass Soukarieh, 2022. "Non-Parametric Conditional U -Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design," Mathematics, MDPI, vol. 11(1), pages 1-69, December.
    37. Gutknecht, Daniel, 2012. "Do Reservation Wages Decline Monotonically? A Novel Statistical Test," Economic Research Papers 270635, University of Warwick - Department of Economics.
    38. Salim Bouzebda & Boutheina Nemouchi, 2023. "Weak-convergence of empirical conditional processes and conditional U-processes involving functional mixing data," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 33-88, April.
    39. Takashi Kamihigashi & John Stachurski, 2012. "Existence, Uniqueness and Stability of Stationary Distributions: An Extension of the Hopenhayn-Prescott Theorem," Discussion Paper Series DP2012-27, Research Institute for Economics & Business Administration, Kobe University.
    40. Seo, Juwon, 2018. "Tests of stochastic monotonicity with improved power," Journal of Econometrics, Elsevier, vol. 207(1), pages 53-70.
    41. Inass Soukarieh & Salim Bouzebda, 2022. "Exchangeably Weighted Bootstraps of General Markov U -Process," Mathematics, MDPI, vol. 10(20), pages 1-42, October.
    42. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.
    43. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

  73. Gaglianone, Wagner Piazza & Linton, Oliver & Lima, Luiz Renato Regis de Oliveira, 2008. "Evaluating Value-at-Risk models via Quantile regressions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 679, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Elena-Ivona DUMITRESCU, 2011. "Backesting Value-at-Risk: From DQ (Dynamic Quantile) to DB (Dynamic Binary) Tests," LEO Working Papers / DR LEO 262, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    2. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    3. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    4. Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," KIER Working Papers 775, Kyoto University, Institute of Economic Research.
    5. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    6. Steven Kou & Xianhua Peng, 2014. "On the Measurement of Economic Tail Risk," Papers 1401.4787, arXiv.org, revised Aug 2015.
    7. So Yeon Chun & Alexander Shapiro & Stan Uryasev, 2012. "Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics," Operations Research, INFORMS, vol. 60(4), pages 739-756, August.
    8. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    9. López-Espinosa, Germán & Moreno, Antonio & Rubia, Antonio & Valderrama, Laura, 2015. "Systemic risk and asymmetric responses in the financial industry," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 471-485.
    10. Jenq-Tzong Shiau & Jia-Wei Lin, 2016. "Clustering Quantile Regression-Based Drought Trends in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1053-1069, February.
    11. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    12. Jenq-Tzong Shiau & Ting-Ju Chen, 2015. "Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2805-2818, June.
    13. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    14. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    15. Tjeerd de Vries, 2021. "A Tale of Two Tails: A Model-free Approach to Estimating Disaster Risk Premia and Testing Asset Pricing Models," Papers 2105.08208, arXiv.org, revised Oct 2023.
    16. Sebastian Bayer & Timo Dimitriadis, 2018. "Regression Based Expected Shortfall Backtesting," Papers 1801.04112, arXiv.org, revised Sep 2019.
    17. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    18. Emese Lazar & Ning Zhang, 2017. "Model Risk of Expected Shortfall," ICMA Centre Discussion Papers in Finance icma-dp2017-10, Henley Business School, University of Reading.
    19. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    20. Zhu, Xuening & Wang, Weining & Wang, Hansheng & Härdle, Wolfgang Karl, 2019. "Network quantile autoregression," Journal of Econometrics, Elsevier, vol. 212(1), pages 345-358.
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    22. Bonga-Bonga, Lumengo & Manguzvane, Mathias Mandla, 2018. "Assessing the extent of contagion of sovereign credit risk among BRICS countries," MPRA Paper 89200, University Library of Munich, Germany.
    23. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    24. De Rezende, Rafael B., 2015. "Risks in macroeconomic fundamentals and excess bond returns predictability," Working Paper Series 295, Sveriges Riksbank (Central Bank of Sweden).
    25. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
    26. Wang, Jying-Nan & Du, Jiangze & Hsu, Yuan-Teng, 2018. "Measuring long-term tail risk: Evaluating the performance of the square-root-of-time rule," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 120-138.
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    28. Ophélie Couperier & Jérémy Leymarie, 2020. "Backtesting Expected Shortfall via Multi-Quantile Regression," Working Papers halshs-01909375, HAL.
    29. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.
    30. Hotta, Luiz Koodi & Trucíos Maza, Carlos César & Pereira, Pedro L. Valls & Zevallos Herencia, Mauricio Henrique, 2024. "Forecasting VaR and ES through Markov-switching GARCH models: does the specication matter?," Textos para discussão 567, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    31. David Kohns & Tibor Szendrei, 2020. "Horseshoe Prior Bayesian Quantile Regression," Papers 2006.07655, arXiv.org, revised Mar 2021.
    32. Marius Galabe Sampid & Haslifah M Hasim & Hongsheng Dai, 2018. "Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-33, June.
    33. Liu, Xiaochun, 2017. "An integrated macro-financial risk-based approach to the stressed capital requirement," Review of Financial Economics, Elsevier, vol. 34(C), pages 86-98.
    34. Gilbert Colletaz & Christophe Hurlin & Christophe Pérignon, 2012. "The Risk Map: A New Tool for Validating Risk Models," Working Papers halshs-00746273, HAL.
    35. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    36. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    37. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    38. Bruno Ferreira Frascaroli & Wellington Charles Lacerda Nobrega, 2019. "Inflation Targeting and Inflation Risk in Latin America," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(11), pages 2389-2408, September.
    39. Elena-Ivona Dumitrescu & Christophe Hurlin & Vinson Pham, 2012. "Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests," Working Papers halshs-00671658, HAL.
    40. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    41. Karmakar, Madhusudan & Paul, Samit, 2019. "Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 699-709.
    42. Fengler, Matthias R. & Herwartz, Helmut, 2015. "Measuring spot variance spillovers when (co)variances are time-varying – the case of multivariate GARCH models," Economics Working Paper Series 1517, University of St. Gallen, School of Economics and Political Science.
    43. Richard Gerlach & Chao Wang, 2016. "Forecasting risk via realized GARCH, incorporating the realized range," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 501-511, April.
    44. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
    45. Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jun 2023.
    46. Liu Xiaochun & Luger Richard, 2018. "Markov-switching quantile autoregression: a Gibbs sampling approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(2), pages 1, April.
    47. Taylor, James W., 2022. "Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio," Journal of Banking & Finance, Elsevier, vol. 140(C).
    48. Hayette Gatfaoui, 2017. "Equity market information and credit risk signaling: A quantile cointegrating regression approach," Post-Print hal-01745285, HAL.
    49. Pradhan, Ashis Kumar & Tiwari, Aviral Kumar, 2021. "Estimating the market risk of clean energy technologies companies using the expected shortfall approach," Renewable Energy, Elsevier, vol. 177(C), pages 95-100.
    50. Daniel Mariño Ustacara & Luis Fernando Melo Velandia, 2016. "Regresión Cuantílica Dinámica para la Medición del Valor en Riesgo: una Aplicación a Datos Colombianos," Borradores de Economia 939, Banco de la Republica de Colombia.
    51. Wagner Piazza Gaglianone & Jaqueline Terra Moura Marins, 2014. "Risk Assessment of the Brazilian FX Rate," Working Papers Series 344, Central Bank of Brazil, Research Department.
    52. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    53. Aramonte, Sirio & Giudice Rodriguez, Marius del & Wu, Jason, 2013. "Dynamic factor Value-at-Risk for large heteroskedastic portfolios," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4299-4309.
    54. Steven Kou & Xianhua Peng, 2016. "On the Measurement of Economic Tail Risk," Operations Research, INFORMS, vol. 64(5), pages 1056-1072, October.
    55. Fresoli, Diego Eduardo & Ruiz Ortega, Esther, 2014. "The uncertainty of conditional returns, volatilities and correlations in DCC models," DES - Working Papers. Statistics and Econometrics. WS ws140202, Universidad Carlos III de Madrid. Departamento de Estadística.
    56. Katherine Uylangco & Siqiwen Li, 2016. "An evaluation of the effectiveness of Value-at-Risk (VaR) models for Australian banks under Basel III," Australian Journal of Management, Australian School of Business, vol. 41(4), pages 699-718, November.
    57. Chao Wang & Qian Chen & Richard Gerlach, 2017. "Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution," Papers 1707.03715, arXiv.org.
    58. Iqbal, Javed, 2017. "Does gold hedge stock market, inflation and exchange rate risks? An econometric investigation," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 1-17.
    59. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    60. Vica Tendenan & Richard Gerlach & Chao Wang, 2020. "Tail risk forecasting using Bayesian realized EGARCH models," Papers 2008.05147, arXiv.org, revised Aug 2020.
    61. Filippo Curti & Marco Migueis, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (U.S.).
    62. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    63. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    64. Sebastian Bayer & Timo Dimitriadis, 2022. "Regression-Based Expected Shortfall Backtesting [Backtesting Expected Shortfall]," Journal of Financial Econometrics, Oxford University Press, vol. 20(3), pages 437-471.
    65. Storti, Giuseppe & Wang, Chao, 2022. "A multivariate semi-parametric portfolio risk optimization and forecasting framework," MPRA Paper 115266, University Library of Munich, Germany.
    66. Chao Wang & Richard Gerlach & Qian Chen, 2018. "A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework," Papers 1807.02422, arXiv.org, revised Jan 2021.
    67. Lúcio Godeiro, Lucas, 2012. "Estimando o VaR (Value-at-Risk) de carteiras via modelos da família GARCH e via Simulação de Monte Carlo [Estimating the VaR (Value-at-Risk) of portfolios via GARCH family models and via Monte Carl," MPRA Paper 45146, University Library of Munich, Germany.
    68. Gerlach, Richard & Abeywardana, Sachin, 2016. "Variational Bayes for assessment of dynamic quantile forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1385-1402.
    69. Chao Wang & Richard Gerlach, 2019. "Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall," Papers 1906.09961, arXiv.org.
    70. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    71. Timo Dimitriadis & Sebastian Bayer, 2017. "A Joint Quantile and Expected Shortfall Regression Framework," Papers 1704.02213, arXiv.org, revised Aug 2017.
    72. Armstrong, Christopher S. & Blouin, Jennifer L. & Jagolinzer, Alan D. & Larcker, David F., 2015. "Corporate governance, incentives, and tax avoidance," Journal of Accounting and Economics, Elsevier, vol. 60(1), pages 1-17.
    73. Chan Jennifer So Kuen & Nitithumbundit Thanakorn & Peiris Shelton & Ng Kok-Haur, 2019. "Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-22, April.
    74. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    75. Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
    76. Giessing, Alexander & He, Xuming, 2019. "On the predictive risk in misspecified quantile regression," Journal of Econometrics, Elsevier, vol. 213(1), pages 235-260.
    77. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
    78. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.
    79. Richard Gerlach & Chao Wang, 2018. "Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures," Papers 1805.08653, arXiv.org.
    80. Cai, Zongwu & Chen, Haiqiang & Liao, Xiaosai, 2023. "A new robust inference for predictive quantile regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 227-250.

  74. Oliver Linton1 & Kyungchul Song & Yoon-Jae Whang, 2008. "Bootstrap Tests of Stochastic Dominance with Asymptotic Similarity on the Boundary," PIER Working Paper Archive 08-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Cited by:

    1. Gonzalo, J. & Olmo, J., 2008. "Testing Downside Risk Efficiency Under Market Distress," Working Papers 08/11, Department of Economics, City University London.
    2. Le-Yu Chen & Jerzy Szroeter, 2009. "Hypothesis testing of multiple inequalities: the method of constraint chaining," CeMMAP working papers CWP13/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Gonzalo, Jesús & Olmo, José, 2009. "Downside Risk Efficiency Under Market Distress," UC3M Working papers. Economics we094423, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Christopher J. Bennett, 2009. "Consistent and Asymptotically Unbiased MinP Tests of Multiple Inequality Moment Restrictions," Vanderbilt University Department of Economics Working Papers 0908, Vanderbilt University Department of Economics.
    5. Martin Huber & Giovanni Mellace, 2015. "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 398-411, May.

  75. Sheng Li & Oliver Linton, 2007. "Evaluating hedge fund performance: a stochastic dominance approach," FMG Discussion Papers dp591, Financial Markets Group.

    Cited by:

    1. Guo, Dongmei & Hu, Yi & Wang, Shouyang & Zhao, Lin, 2016. "Comparing risks with reference points: A stochastic dominance approach," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 105-116.

  76. Gregory Connor & Matthias Hagmann & Oliver Linton, 2007. "Efficient Estimation of a Semiparametric Characteristic- Based Factor Model of Security Returns," Swiss Finance Institute Research Paper Series 07-26, Swiss Finance Institute.

    Cited by:

    1. Zhang, Lyuou & Zhou, Wen & Wang, Haonan, 2021. "A semiparametric latent factor model for large scale temporal data with heteroscedasticity," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    2. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "Estimation of large dimensional conditional factor models in finance," Working Papers unige:125031, University of Geneva, Geneva School of Economics and Management.
    3. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Characteristic Effects of Large Continuous-Time Linear Models," Papers 1711.04392, arXiv.org, revised Dec 2018.
    4. Kunpeng Li & Qi Li & Lina Lu, 2018. "Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models," Supervisory Research and Analysis Working Papers RPA 18-2, Federal Reserve Bank of Boston.
    5. Patrick GAGLIARDINI & Christian GOURIEROUX, 2009. "Efficiency in Large Dynamic Panel Models with Common Factor," Swiss Finance Institute Research Paper Series 09-12, Swiss Finance Institute.
    6. French, Declan & Wu, Yuliang & Li, Youwei, 2016. "Identifying the relative importance of stock characteristics," Journal of Multinational Financial Management, Elsevier, vol. 34(C), pages 80-91.
    7. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    8. Jaeheon Jung, 2019. "Estimating a Large Covariance Matrix in Time-varying Factor Models," Papers 1910.11965, arXiv.org.
    9. Yang, Shuquan & Ling, Nengxiang, 2023. "Robust projected principal component analysis for large-dimensional semiparametric factor modeling," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    10. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.
    11. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.
    12. Georg Keilbar & Juan M. Rodriguez-Poo & Alexandra Soberon & Weining Wang, 2022. "A semiparametric approach for interactive fixed effects panel data models," Papers 2201.11482, arXiv.org, revised Mar 2023.
    13. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    14. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.

  77. Kalnina, Ilze & Linton, Oliver, 2007. "Inference about realized volatility using infill subsampling," LSE Research Online Documents on Economics 4411, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    2. Jean Jacod & Yingying Li & Per A. Mykland & Mark Podolskij & Mathias Vetter, 2007. "Microstructure Noise in the Continuous Case: The Pre-Averaging Approach - JLMPV-9," CREATES Research Papers 2007-43, Department of Economics and Business Economics, Aarhus University.
    3. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2007. "Microstructure noise in the continuous case: the pre-averaging approach," Technical Reports 2007,41, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Mykland, Per A. & Zhang, Lan, 2016. "Between data cleaning and inference: Pre-averaging and robust estimators of the efficient price," Journal of Econometrics, Elsevier, vol. 194(2), pages 242-262.

  78. Linton, Oliver & Mammen, Enno, 2006. "Nonparametric transformation to white noise," LSE Research Online Documents on Economics 4426, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    2. Oliver Linton & Qiying Wang, 2013. "Non-parametric transformation regression with non-stationary data," CeMMAP working papers CWP16/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Gagliardini, Patrick & Scaillet, Olivier, 2012. "Tikhonov regularization for nonparametric instrumental variable estimators," Journal of Econometrics, Elsevier, vol. 167(1), pages 61-75.
    4. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Gregory Connor & Oliver Linton & Matthias Hagmann, 2007. "Efficient Estimation of a Semiparametric Characteristic-Based Factor Model of Security Returns," FMG Discussion Papers dp599, Financial Markets Group.
    6. Koo, Bonsoo & Linton, Oliver, 2012. "Estimation of semiparametric locally stationary diffusion models," Journal of Econometrics, Elsevier, vol. 170(1), pages 210-233.
    7. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Juliane Geller & Michael H. Neumann, 2018. "Improved local polynomial estimation in time series regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-27, January.
    9. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    10. Ozabaci, Deniz & Henderson, Daniel J., 2014. "Additive Kernel Estimates of Returns to Schooling," IZA Discussion Papers 8736, Institute of Labor Economics (IZA).
    11. Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019. "Conditional Variance Forecasts for Long-Term Stock Returns," Risks, MDPI, vol. 7(4), pages 1-22, November.
    12. Oliver Linton & Qiying Wang, 2013. "Non-parametric transformation regression with non-stationary data," CeMMAP working papers 16/13, Institute for Fiscal Studies.
    13. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020. "Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    14. Mammen, Enno & Martínez-Miranda, María Dolores & Nielsen, Jens Perch & Vogt, Michael, 2021. "Calendar effect and in-sample forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 31-52.
    15. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns – The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.
    16. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020. "Short-Term Exuberance and long-term stability: A simultaneous optimization of stock return predictions for short and long horizons," Graz Economics Papers 2020-20, University of Graz, Department of Economics.
    17. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.
    18. Liangjun Su & Aman Ullah & Yun Wang, 2013. "Nonparametric regression estimation with general parametric error covariance: a more efficient two-step estimator," Empirical Economics, Springer, vol. 45(2), pages 1009-1024, October.

  79. Kalnina, Ilze & Linton, Oliver, 2006. "Estimating quadratic variation consistently in the presence of correlated measurement error," LSE Research Online Documents on Economics 4413, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Neil Shephard & Dacheng Xiu, 2012. "Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices," Economics Series Working Papers 604, University of Oxford, Department of Economics.
    2. Kalnina, Ilze & Linton, Oliver, 2007. "Inference about realized volatility using infill subsampling," LSE Research Online Documents on Economics 4411, London School of Economics and Political Science, LSE Library.
    3. Ole E Barndorff-Nielsen & Peter Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," OFRC Working Papers Series 2006fe05, Oxford Financial Research Centre.
    4. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2009. "Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading," Global COE Hi-Stat Discussion Paper Series gd08-037, Institute of Economic Research, Hitotsubashi University.
    5. Neil Shephard & Ole E. Barndorff-Nielsen & Asger Lunde, 2006. "Subsampling realised kernels," Economics Series Working Papers 278, University of Oxford, Department of Economics.
    6. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    7. Lidan Grossmass, 2014. "Obtaining and Predicting the Bounds of Realized Correlations," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(III), pages 191-226, September.
    8. Elezovic, Suad, 2009. "Functional modelling of volatility in the Swedish limit order book," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2107-2118, April.
    9. O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.

  80. Connor, Gregory & Linton, Oliver, 2006. "Semiparametric estimation of a characteristic-based factor model of common stock returns," LSE Research Online Documents on Economics 4424, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Charle Augusto Londoño, 2011. "Regresión del cuantil aplicada al modelo de redes neuronales artificiales," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(64), pages 62-109, July.
    2. Sakemoto, Ryuta, 2019. "Currency carry trades and the conditional factor model," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 198-208.
    3. Zhang, Lyuou & Zhou, Wen & Wang, Haonan, 2021. "A semiparametric latent factor model for large scale temporal data with heteroscedasticity," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    4. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    5. Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
    6. Sung Hoon Choi, 2021. "Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia," Papers 2108.10250, arXiv.org, revised May 2022.
    7. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    8. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "Estimation of large dimensional conditional factor models in finance," Working Papers unige:125031, University of Geneva, Geneva School of Economics and Management.
    9. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Characteristic Effects of Large Continuous-Time Linear Models," Papers 1711.04392, arXiv.org, revised Dec 2018.
    10. Gregory Connor & Oliver Linton & Matthias Hagmann, 2007. "Efficient Estimation of a Semiparametric Characteristic-Based Factor Model of Security Returns," FMG Discussion Papers dp599, Financial Markets Group.
    11. Kunpeng Li & Qi Li & Lina Lu, 2018. "Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models," Supervisory Research and Analysis Working Papers RPA 18-2, Federal Reserve Bank of Boston.
    12. Sainan Jin & Liangjun Su & Yonghui Zhang, 2015. "Nonparametric testing for anomaly effects in empirical asset pricing models," Empirical Economics, Springer, vol. 48(1), pages 9-36, February.
    13. French, Declan & Wu, Yuliang & Li, Youwei, 2016. "Identifying the relative importance of stock characteristics," Journal of Multinational Financial Management, Elsevier, vol. 34(C), pages 80-91.
    14. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    15. Jaeheon Jung, 2019. "Estimating a Large Covariance Matrix in Time-varying Factor Models," Papers 1910.11965, arXiv.org.
    16. Dolado, Juan J & Chen, Liang & Gonzalo, Jesus & Pan, Haozi, 2023. "Estimation of Characteristics-based Quantile Factor Models," CEPR Discussion Papers 18115, C.E.P.R. Discussion Papers.
    17. Yang, Shuquan & Ling, Nengxiang, 2023. "Robust projected principal component analysis for large-dimensional semiparametric factor modeling," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    18. Xiang, Jingjie & Li, Kunpeng & Cui, Guowei, 2018. "A note on the asymptotic properties of least squares estimation in high dimensional constrained factor models," Economics Letters, Elsevier, vol. 171(C), pages 144-148.
    19. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.
    20. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
    21. Szymon Borak & Wolfgang Härdle & Enno Mammen & Byeong U. Park, 2007. "Time Series Modelling with Semiparametric Factor Dynamics," SFB 649 Discussion Papers SFB649DP2007-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    22. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas," Departmental Working Papers 201711, Rutgers University, Department of Economics.
    23. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.
    24. Georg Keilbar & Juan M. Rodriguez-Poo & Alexandra Soberon & Weining Wang, 2022. "A semiparametric approach for interactive fixed effects panel data models," Papers 2201.11482, arXiv.org, revised Mar 2023.
    25. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    26. Shujie Ma & Oliver Linton & Jiti Gao, 2017. "Estimation and inference in semiparametric quantile factor models," Monash Econometrics and Business Statistics Working Papers 8/17, Monash University, Department of Econometrics and Business Statistics.
    27. Matias D. Cattaneo & Richard K. Crump & Weining Wang, 2023. "Beta-Sorted Portfolios," Staff Reports 1068, Federal Reserve Bank of New York.
    28. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.
    29. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    30. Lee, Jiyon, 2015. "A semiparametric single index model with heterogeneous impacts on an unobserved variable," Journal of Econometrics, Elsevier, vol. 184(1), pages 13-36.

  81. David Jacho-Chavez & Arthur Lewbel & Oliver Linton, 2006. "Identification and Nonparametric Estimation of a Transformed Additively Separable Model," Boston College Working Papers in Economics 652, Boston College Department of Economics, revised 26 Nov 2008.

    Cited by:

    1. Colling, Benjamin & Van Keilegom, Ingrid, 2016. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," LIDAM Discussion Papers ISBA 2016031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Joel L. Horowitz & Sokbae (Simon) Lee, 2015. "Nonparametric estimation and inference under shape restrictions," CeMMAP working papers CWP67/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. David Jacho-Chavez & Arthur Lewbel & Oliver Linton, 2006. "Identification and Nonparametric Estimation of a Transformed Additively Separable Model," Boston College Working Papers in Economics 652, Boston College Department of Economics, revised 26 Nov 2008.
    4. Vanhems, Anne & Van Keilegom, Ingrid, 2013. "Semiparametric transformation model with endogeneity: a control function approach," LIDAM Discussion Papers ISBA 2013018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Ruixuan Liu, 2020. "A competing risks model with time‐varying heterogeneity and simultaneous failure," Quantitative Economics, Econometric Society, vol. 11(2), pages 535-577, May.
    6. Joel L. Horowitz & Sokbae (Simon) Lee, 2015. "Nonparametric estimation and inference under shape restrictions," CeMMAP working papers 67/15, Institute for Fiscal Studies.
    7. Koohyun Kwon & Soonwoo Kwon, 2020. "Adaptive Inference in Multivariate Nonparametric Regression Models Under Monotonicity," Papers 2011.14219, arXiv.org.
    8. Joel L. Horowitz & Sokbae (Simon) Lee, 2016. "Nonparametric estimation and inference under shape restrictions," CeMMAP working papers 29/16, Institute for Fiscal Studies.
    9. Lewbel, Arthur & Lu, Xun & Su, Liangjun, 2015. "Specification testing for transformation models with an application to generalized accelerated failure-time models," Journal of Econometrics, Elsevier, vol. 184(1), pages 81-96.
    10. Van Keilegom, Ingrid & Vanhems, Anne, 2011. "Semiparametric transformation model with endogeneity: a control function approach," TSE Working Papers 11-243, Toulouse School of Economics (TSE).
    11. Krief, Jerome M., 2017. "Direct instrumental nonparametric estimation of inverse regression functions," Journal of Econometrics, Elsevier, vol. 201(1), pages 95-107.
    12. Christoph Breunig & Stephan Martin, 2020. "Nonclassical Measurement Error in the Outcome Variable," Papers 2009.12665, arXiv.org, revised May 2021.
    13. Vanhems, Anne & Van Keilegom, Ingrid, 2011. "Semiparametric transformation model with endogeneity: a control function approach," LIDAM Discussion Papers ISBA 2011011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Pierre-Andre Chiappori & Ivana Komunjer & Dennis Kristensen, 2011. "Nonparametric Identification and Estimation of Transformation Models," CAM Working Papers 2011-01, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    15. Vanhems, Anne & Van Keilegom, Ingrid, 2019. "Estimation Of A Semiparametric Transformation Model In The Presence Of Endogeneity," Econometric Theory, Cambridge University Press, vol. 35(1), pages 73-110, February.
    16. Centorrino, Samuele & Florens, Jean-Pierre, 2021. "Nonparametric Instrumental Variable Estimation of Binary Response Models with Continuous Endogenous Regressors," Econometrics and Statistics, Elsevier, vol. 17(C), pages 35-63.
    17. Colling, Benjamin & Van Keilegom, Ingrid, 2017. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 10-30.

  82. Yoon-Jae Whang & Young-Hyun Cho & Oliver Linton, 2006. "Are there Monday effects in Stock Returns: A Stochastic Dominance Approach," FMG Discussion Papers dp568, Financial Markets Group.

    Cited by:

    1. Chuan-Hao Hsu & Hung-Gay Fung & Yi-Ping Chang, 2016. "The performance of Taiwanese firms after a share repurchase announcement," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1251-1269, November.
    2. Chundakkadan, Radeef & Nedumparambil, Elizabeth, 2022. "In search of COVID-19 and stock market behavior," Global Finance Journal, Elsevier, vol. 54(C).
    3. Christos Kollias & Stephanos Papadamou, 2012. "Rogue State Behavior and Markets: The Financial Fallout of North Korean Nuclear Tests," Economics of Security Working Paper Series 67, DIW Berlin, German Institute for Economic Research.
    4. Kaplanski, Guy & Levy, Haim, 2010. "Sentiment and stock prices: The case of aviation disasters," Journal of Financial Economics, Elsevier, vol. 95(2), pages 174-201, February.
    5. Chatzitzisi, Evanthia & Fountas, Stilianos & Panagiotidis, Theodore, 2021. "Another look at calendar anomalies," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 823-840.
    6. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    7. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    8. Zhuo Qiao & Wing-Keung Wong, 2015. "Which is a better investment choice in the Hong Kong residential property market: a big or small property?," Applied Economics, Taylor & Francis Journals, vol. 47(16), pages 1670-1685, April.
    9. Linton, O. & Wu, J., 2016. "A coupled component GARCH model for intraday and overnight volatility," Cambridge Working Papers in Economics 1671, Faculty of Economics, University of Cambridge.
    10. Ichev, Riste & Marinč, Matej, 2018. "Stock prices and geographic proximity of information: Evidence from the Ebola outbreak," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 153-166.
    11. Qiao, Zhuo & Pukthuanthong, Kuntara, 2019. "Has the difference in stock liquidity and stock returns between Chinese state owned and privately owned enterprises become smaller?," Finance Research Letters, Elsevier, vol. 28(C), pages 39-44.
    12. Laurence E. Blose & Vijay Gondhalekar, 2013. "Weekend gold returns in bull and bear markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(3), pages 609-622, September.
    13. Robert Elliott & Ying Zhou, 2013. "State-Owned Enterprises, Exporting and Productivity in China: A Stochastic Dominance Approach," Discussion Papers 13-03, Department of Economics, University of Birmingham.
    14. Sheng-Ping Yang & Thanh Nguyen, 2019. "Skewness Preference and Asset Pricing: Evidence from the Japanese Stock Market," JRFM, MDPI, vol. 12(3), pages 1-10, September.
    15. Scherf, Matthias & Matschke, Xenia & Rieger, Marc Oliver, 2022. "Stock market reactions to COVID-19 lockdown: A global analysis," Finance Research Letters, Elsevier, vol. 45(C).
    16. Kaiser, Lars, 2019. "Seasonality in cryptocurrencies," Finance Research Letters, Elsevier, vol. 31(C).
    17. Irshad Hira & Taib Hasniza Mohd & Hussain Haroon & Hussain Rana Yassir, 2023. "Conventional and Islamic Equity Market Reaction Towards Terrorism: Evidence Based on Target Types, Location and Islamic Calendar Months," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 33(4), pages 70-116, December.
    18. Marshall, Ben R. & Nguyen, Hung T. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat & Young, Martin, 2021. "Do climate risks matter for green investment?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    19. Chui, David & Wing Cheng, Wui & Chi Chow, Sheung & LI, Ya, 2020. "Eastern Halloween effect: A stochastic dominance approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 68(C).
    20. Levy, Tamir & Yagil, Joseph, 2012. "The week-of-the-year effect: Evidence from around the globe," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1963-1974.
    21. Al-Khazali, Osamah & Mirzaei, Ali, 2017. "Stock market anomalies, market efficiency and the adaptive market hypothesis: Evidence from Islamic stock indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 190-208.
    22. Kim Chang Sik, 2009. "Test for Spatial Dominances in the Distribution of Stock Returns: Evidence from the Korean Stock Market Before and After the East Asian Financial Crisis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(4), pages 1-27, September.
    23. Radeef Chundakkadan, 2021. "Light a lamp and look at the stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
    24. Olfa Chaouachi & Imen Dhaou, 2020. "The Day of the Week Effect: Unconditional and Conditional Market Risk Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 10(6), pages 94-98.
    25. Cohen, Gil, 2014. "Why don’t you trade only four days a year? An empirical study into the abnormal returns of quarters first trading day," Economics Letters, Elsevier, vol. 124(3), pages 335-337.
    26. Qadan, Mahmoud & Idilbi-Bayaa, Yasmeen, 2021. "The day-of-the-week-effect on the volatility of commodities," Resources Policy, Elsevier, vol. 71(C).
    27. Eunhee Lee & Chang Kim & In-Moo Kim, 2015. "Equity premium over different investment horizons," Empirical Economics, Springer, vol. 48(3), pages 1169-1187, May.
    28. Audrius Kabašinskas & Lina Kadikinaitė, 2016. "The construction of an investment portfolio using stochastic programming," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 6(3), pages 151-160, July.
    29. Ülkü, Numan & Rogers, Madeline, 2018. "Who drives the Monday effect?," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 46-65.
    30. Sungro Lee, Chang Sik Kim, In-Moo Kim & Chang Sik Kim & In-Moo Kim, 2012. "Testing the Monday Effect using High-frequency Intraday Returns: A Spatial Dominance Approach," Korean Economic Review, Korean Economic Association, vol. 28, pages 69-90.
    31. Santi, Caterina & Zwinkels, Remco C.J., 2023. "Exploring style herding by mutual funds," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    32. Ali CELÝK, 2021. "Volatility of BIST 100 Returns After 2020, Calendar Anomalies and COVID-19 Effect," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 15(1), pages 61-81.
    33. Kapalczynski, Anna & Lien, Donald, 2021. "Effectiveness of Augmented Dollar-Cost Averaging," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    34. Kumar, Satish, 2016. "Revisiting calendar anomalies: Three decades of multicurrency evidence," Journal of Economics and Business, Elsevier, vol. 86(C), pages 16-32.
    35. McSweeney, Brendan, 2009. "The roles of financial asset market failure denial and the economic crisis: Reflections on accounting and financial theories and practices," Accounting, Organizations and Society, Elsevier, vol. 34(6-7), pages 835-848, August.
    36. Jeffrey E. Jarrett, 2008. "Predicting Daily Stock Returns: A Lengthy Study of the Hong Kong and Tokyo Stock Exchanges," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 7(1), pages 37-51, April.
    37. Annaert, Jan & Osselaer, Sofieke Van & Verstraete, Bert, 2009. "Performance evaluation of portfolio insurance strategies using stochastic dominance criteria," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 272-280, February.
    38. Al-Khazali, Osamah & Lean, Hooi Hooi & Samet, Anis, 2014. "Do Islamic stock indexes outperform conventional stock indexes? A stochastic dominance approach," Pacific-Basin Finance Journal, Elsevier, vol. 28(C), pages 29-46.
    39. Erling Røed Larsen, 2021. "Intra‐Week Price Patterns in the Housing Market," Scandinavian Journal of Economics, Wiley Blackwell, vol. 123(1), pages 327-352, January.
    40. Al-Khazali, Osamah, 2014. "Revisiting fast profit investor sentiment and stock returns during Ramadan," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 158-170.

  83. Linton, Oliver & Seo, Myunghwan, 2005. "A smoothed least squares estimator for threshold regression models," LSE Research Online Documents on Economics 4434, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Kemp, Gordon C.R. & Santos Silva, J.M.C., 2012. "Regression towards the mode," Journal of Econometrics, Elsevier, vol. 170(1), pages 92-101.
    2. Chen, Haiqiang & Li, Yingxing & Lin, Ming & Zhu, Yanli, 2018. "A Regime Shift Model with Nonparametric Switching Mechanism," IRTG 1792 Discussion Papers 2018-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Yélou, Clément & Larue, Bruno & Tran, Kien C., 2010. "Threshold effects in panel data stochastic frontier models of dairy production in Canada," Economic Modelling, Elsevier, vol. 27(3), pages 641-647, May.
    4. Young-Joo Kim & Myung Hwan Seo, 2017. "Is There a Jump in the Transition?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 241-249, April.
    5. Miao, Ke & Su, Liangjun & Wang, Wendun, 2020. "Panel threshold regressions with latent group structures," Journal of Econometrics, Elsevier, vol. 214(2), pages 451-481.
    6. Donayre Luiggi & Eo Yunjong & Morley James, 2018. "Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(1), pages 1-11, February.
    7. Li, Dong & Tong, Howell, 2016. "Nested sub-sample search algorithm for estimation of threshold models," LSE Research Online Documents on Economics 68880, London School of Economics and Political Science, LSE Library.
    8. Rothfelder, Mario & Boldea, Otilia, 2019. "Testing for a Threshold in Models with Endogenous Regressors," Other publications TiSEM 94a7c921-f27f-43a0-82f4-4, Tilburg University, School of Economics and Management.
    9. Li, Dong & Ling, Shiqing, 2012. "On the least squares estimation of multiple-regime threshold autoregressive models," Journal of Econometrics, Elsevier, vol. 167(1), pages 240-253.
    10. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    11. Daniel Ordoñez-Callamand & Luis F. Melo-Velandia & Oscar M. Valencia-Arana, 2017. "Current Account Sustainability in Latin America Considering Nonlinearities," Borradores de Economia 987, Banco de la Republica de Colombia.
    12. Dong Li & Shiqing Ling & Rongmao Zhang, 2016. "On a Threshold Double Autoregressive Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 68-80, January.
    13. Laurent Callot & Mehmet Caner & Anders Bredahl Kock & Juan Andres Riquelme, 2015. "Sharp Threshold Detection Based on Sup-norm Error rates in High-dimensional Models," CREATES Research Papers 2015-10, Department of Economics and Business Economics, Aarhus University.
    14. Olaoye, Olumide O. & Eluwole, Oluwatosin O. & Ayesha, Aziz & Afolabi, Olugbenga O., 2020. "Government spending and economic growth in ECOWAS: An asymmetric analysis," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    15. Bruce E. Hansen, 2017. "Regression Kink With an Unknown Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 228-240, April.
    16. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2017. "Correction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 883-883, April.
    17. Yoonseok Lee & Yulong Wang, 2020. "Inference in Threshold Models," Center for Policy Research Working Papers 223, Center for Policy Research, Maxwell School, Syracuse University.
    18. Egger, Peter & Erhardt, Katharina & Keuschnigg, Christian, 2014. "Heterogeneous Tax Sensitivity of Firm-level Investments," Economics Series 306, Institute for Advanced Studies.
    19. Sadikoglu, Serhan, 2019. "Essays in econometric theory," Other publications TiSEM 99d83644-f9dc-49e3-a4e1-5, Tilburg University, School of Economics and Management.
    20. Yoonseok Lee & Yulong Wang, 2019. "Threshold Regression with Nonparametric Sample Splitting," Papers 1905.13140, arXiv.org, revised Jan 2021.
    21. Andros Kourtellos & Thanasis Stengos & Yiguo Sun, 2017. "Endogeneity in Semiparametric Threshold Regression," University of Cyprus Working Papers in Economics 10-2017, University of Cyprus Department of Economics.
    22. Andros Kourtellos & Thanasis Stengos & Chih Ming Tan, 2011. "Structural Threshold Regression," University of Cyprus Working Papers in Economics 13-2011, University of Cyprus Department of Economics.
    23. Chen, Haiqiang & Li, Yingxing & Lin, Ming & Zhu, Yanli, 2018. "A Regime Shift Model with Nonparametric Switching Mechanism," IRTG 1792 Discussion Papers 2018-048, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    24. Chaoyi Chen & Yiguo Sun & Yao Rao, 2023. "Threshold MIDAS Forecasting of Inflation Rate," Working Papers 202314, University of Liverpool, Department of Economics.
    25. Jesús Gonzalo & Jean-Yves Pitarakis, 2013. "Estimation and inference in threshold type regime switching models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 8, pages 189-205, Edward Elgar Publishing.
    26. Lee, Sokbae & Seo, Myung Hwan, 2007. "Semiparametric estimation of a binary response model with a change-point due to a covariate threshold," LSE Research Online Documents on Economics 6806, London School of Economics and Political Science, LSE Library.
    27. N. R. Ramírez-Rondán, 2020. "Maximum likelihood estimation of dynamic panel threshold models," Econometric Reviews, Taylor & Francis Journals, vol. 39(3), pages 260-276, March.
    28. Haiqiang Chen, "undated". "Robust Estimation and Inference for Threshold Models with Integrated Regressors," Working Papers 2013-12-02, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    29. Myung Hwan Seo & Yongcheol Shin, 2014. "Dynamic Panels with Threshold Effect and Endogeneity," STICERD - Econometrics Paper Series 577, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    30. Hong, Han & Mahajan, Aprajit & Nekipelov, Denis, 2015. "Extremum estimation and numerical derivatives," Journal of Econometrics, Elsevier, vol. 188(1), pages 250-263.
    31. Jiatong Li & Hongqiang Yan, 2024. "Uniform Inference in High-Dimensional Threshold Regression Models," Papers 2404.08105, arXiv.org.
    32. Shuyu Li & Rongrong Li, 2021. "Revisiting the Existence of EKC Hypothesis under Different Degrees of Population Aging: Empirical Analysis of Panel Data from 140 Countries," IJERPH, MDPI, vol. 18(23), pages 1-19, December.
    33. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    34. Hidalgo, Javier & Lee, Jungyoon & Seo, Myung Hwan, 2019. "Robust inference for threshold regression models," LSE Research Online Documents on Economics 100333, London School of Economics and Political Science, LSE Library.
    35. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Factor-Driven Two-Regime Regression," Department of Economics Working Papers 2018-14, McMaster University.
    36. Qusai Mohammad Qasim Alabed & Fathin Faizah Said & Zulkefly Abdul Karim & Mohd Azlan Shah Zaidi & Mohammed Daher Alshammary, 2021. "Energy–Growth Nexus in the MENA Region: A Dynamic Panel Threshold Estimation," Sustainability, MDPI, vol. 13(22), pages 1-18, November.
    37. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    38. Iglesias Emma M, 2010. "First and Second Order Asymptotic Bias Correction of Nonlinear Estimators in a Non-Parametric Setting and an Application to the Smoothed Maximum Score Estimator," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-30, May.
    39. Duan Lianjie, 2023. "Export Cutoff Productivity, Uncertainty and Duration of Waiting for Exporting," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 17(1), pages 1-19, January.
    40. Andros Kourtellos & Thanasis Stengos & Chih Ming Tan, 2009. "Do Institutions Rule? The Role of Heterogeneity in the Institutions vs. Geography Debate," Working Papers 0910, University of Guelph, Department of Economics and Finance.
    41. Miao, Ke & Li, Kunpeng & Su, Liangjun, 2020. "Panel threshold models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 219(1), pages 137-170.
    42. Chaoyi Chen & Yiguo Sun, 2018. "Monte Carlo Comparison for Nonparametric Threshold Estimators," JRFM, MDPI, vol. 11(3), pages 1-15, August.
    43. Daniel J. Henderson & Christopher F. Parmeter & Liangjun Su, 2017. "M-Estimation of a Nonparametric Threshold Regression Model," Working Papers 2017-15, University of Miami, Department of Economics.
    44. Yu, Ping, 2015. "Adaptive estimation of the threshold point in threshold regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 83-100.
    45. Alogoskoufis, George & Malliaris, A.G. & Stengos, Thanasis, 2023. "The scope and methodology of economic and financial asymmetries," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    46. Hasanov, Fakhri J. & Aliyev, Ruslan & Taskin, Dilvin & Suleymanov, Elchin, 2023. "Oil rents and non-oil economic growth in CIS oil exporters. The role of financial development," Resources Policy, Elsevier, vol. 82(C).
    47. Kourtellos, A. & Tan, C.M. & Stengos, T., 2008. "THRET: Threshold Regression with Endogenous Threshold Variables," Working Papers 0801, University of Guelph, Department of Economics and Finance.
    48. Massacci, Daniele, 2013. "A switching model with flexible threshold variable: With an application to nonlinear dynamics in stock returns," Economics Letters, Elsevier, vol. 119(2), pages 199-203.
    49. Rothfelder, Mario P. & Boldea, Otilia, 2022. "Testing for a Threshold in Models with Endogenous Regressors," Other publications TiSEM 674deead-8826-450a-8f56-f, Tilburg University, School of Economics and Management.
    50. Chlibi Souhir & Jawadi Fredj & Sellami Mohamed, 2017. "Modeling threshold effects in stock price co-movements: a vector nonlinear cointegration approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 47-63, February.
    51. Zhu Yanli & Chen Haiqiang & Lin Ming, 2019. "Threshold models with time-varying threshold values and their application in estimating regime-sensitive Taylor rules," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(5), pages 1-17, December.

  84. Post, G.T. & Linton, O. & Whang, Y-J., 2005. "Testing for Stochastic Dominance Efficiency," ERIM Report Series Research in Management ERS-2005-033-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Arvanitis, Stelios & Topaloglou, Nikolas, 2017. "Testing for prospect and Markowitz stochastic dominance efficiency," Journal of Econometrics, Elsevier, vol. 198(2), pages 253-270.
    2. Duc Khuong Nguyen & Nikolas Topaloglou & Thomas Walther, 2020. "Asset Classes and Portfolio Diversification: Evidence from a Stochastic Spanning Approach," Working Papers 2020-009, Department of Research, Ipag Business School.
    3. Sungro Lee, Chang Sik Kim, In-Moo Kim & Chang Sik Kim & In-Moo Kim, 2012. "Testing the Monday Effect using High-frequency Intraday Returns: A Spatial Dominance Approach," Korean Economic Review, Korean Economic Association, vol. 28, pages 69-90.

  85. Woocheol Kim & Oliver Linton, 2004. "A Local Instrumental Variable Estimation Method For Generalized Additive Volatility Models," FMG Discussion Papers dp509, Financial Markets Group.

    Cited by:

    1. Chen, Bin & Song, Zhaogang, 2013. "Testing whether the underlying continuous-time process follows a diffusion: An infinitesimal operator-based approach," Journal of Econometrics, Elsevier, vol. 173(1), pages 83-107.
    2. Chen, Bin & Hong, Yongmiao, 2012. "Testing For The Markov Property In Time Series," Econometric Theory, Cambridge University Press, vol. 28(1), pages 130-178, February.
    3. Heejoon Han & Shen Zhang, 2012. "Non‐stationary non‐parametric volatility model," Econometrics Journal, Royal Economic Society, vol. 15(2), pages 204-225, June.

  86. Oliver Linton & Yoon-Jae Whang, 2004. "A Quantilogram Approach to Evaluating Directional Predictability," Cowles Foundation Discussion Papers 1454, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Jaehun Chung & Yongmiao Hong, 2007. "Model-free evaluation of directional predictability in foreign exchange markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 855-889.
    2. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, New Economic School (NES).
    3. Gilbert W. Bassett, 2004. "Pessimistic Portfolio Allocation and Choquet Expected Utility," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 477-492.
    4. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    5. Gilbert W. Bassett Jr Bassett & Roger Koenker & Gregory Kordas, 2004. "Pessimistic portfolio allocation and Choquet expected utility," CeMMAP working papers 09/04, Institute for Fiscal Studies.
    6. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.

  87. Benoit Perron & Oliver Linton, 2004. "The Shape of the Risk Premium: Evidence from a Semiparametric GARCH Model," FMG Discussion Papers dp514, Financial Markets Group.

    Cited by:

    1. Christensen, Bent Jesper & Dahl, Christian M. & Iglesias, Emma M., 2012. "Semiparametric inference in a GARCH-in-mean model," Journal of Econometrics, Elsevier, vol. 167(2), pages 458-472.
    2. Ghosh, Anisha & Linton, Oliver, 2007. "Consistent estimation of the risk-return tradeoff in the presence of measurement error," LSE Research Online Documents on Economics 24506, London School of Economics and Political Science, LSE Library.

  88. Kim, Woocheol & Linton, Oliver B., 2004. "The live method for generalized additive volatility models," LSE Research Online Documents on Economics 321, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Xu, Ke-Li & Phillips, Peter C. B., 2011. "Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 518-528.
    2. Peter C.B. Phillips & Ke-Li Xu, 2007. "Tilted Nonparametric Estimation of Volatility Functions," Cowles Foundation Discussion Papers 1612, Cowles Foundation for Research in Economics, Yale University, revised Jul 2010.
    3. Douglas Gomes dos Santos & Flávio Augusto Ziegelmann, 2008. "Estimação de volatilidade em períodos de crise: Modelos aditivos semi-paramétricos versus modelos versus modelo Garch," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807201932370, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].

  89. Enno Mammen & Oliver Linton, 2004. "Estimating Semiparametric ARCH Models by Kernel Smoothing Methods," FMG Discussion Papers dp511, Financial Markets Group.

    Cited by:

    1. Perron, Benoit, 2004. "Détection non paramétrique de sauts dans la volatilité des marchés financiers," L'Actualité Economique, Société Canadienne de Science Economique, vol. 80(2), pages 229-251, Juin-Sept.
    2. Linton, Oliver & Srisuma, Sorawoot, 2010. "Semiparametric estimation of Markov decision processeswith continuous state space," LSE Research Online Documents on Economics 58187, London School of Economics and Political Science, LSE Library.
    3. Woocheol Kim, 2004. "Identification And Estimation Of Nonparametric Structural," Econometric Society 2004 Far Eastern Meetings 733, Econometric Society.
    4. Linton, Oliver & Mammen, Enno, 2004. "Estimating semiparametric ARCH (∞) models by kernel smoothing methods," LSE Research Online Documents on Economics 24762, London School of Economics and Political Science, LSE Library.

  90. Michael Sabbatini & Oliver Linton, 2004. "A GARCH Model of the Implied Volatility of the Swiss Market Index From Option Pricesdffrom Options Prices," FMG Discussion Papers dp516, Financial Markets Group.

    Cited by:

    1. Kiyotaka Satoyoshi & Hidetoshi Mitsui, 2011. "Empirical Study of Nikkei 225 Options with the Markov Switching GARCH Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 18(1), pages 55-68, March.

  91. Oliver Linton, 2004. "Nonparametric inference for unbalance time series data," CeMMAP working papers CWP06/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Phillips, Peter C.B., 2005. "Automated Discovery In Econometrics," Econometric Theory, Cambridge University Press, vol. 21(1), pages 3-20, February.
    2. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173.

  92. Linton, Oliver & Mammen, Enno, 2003. "Estimating semiparametric ARCH (∞) models by kernel smoothing methods," LSE Research Online Documents on Economics 58068, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers CWP24/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Meister, Alexander & Kreiß, Jens-Peter, 2016. "Statistical inference for nonparametric GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 126(10), pages 3009-3040.
    3. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    4. Ai, Chunrong & Chen, Xiaohong, 2007. "Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables," Journal of Econometrics, Elsevier, vol. 141(1), pages 5-43, November.
    5. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers 24/12, Institute for Fiscal Studies.
    6. Dufour, Jean-Marie & García, René & Taamouti, Abderrahim, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Degui Li & Zudi Lu & Oliver Linton, 2011. "Local Linear Fitting Under Near Epoch Dependence: Uniform consistency with Convergence Rates," Monash Econometrics and Business Statistics Working Papers 16/11, Monash University, Department of Econometrics and Business Statistics.
    8. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    9. Gagliardini, Patrick & Scaillet, Olivier, 2012. "Tikhonov regularization for nonparametric instrumental variable estimators," Journal of Econometrics, Elsevier, vol. 167(1), pages 61-75.
    10. Fengler, M.R. & Mammen, E. & Vogt, M., 2015. "Specification and structural break tests for additive models with applications to realized variance data," Journal of Econometrics, Elsevier, vol. 188(1), pages 196-218.
    11. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. Iglesias, Emma M. & Linton, Oliver, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," UC3M Working papers. Economics we094726, Universidad Carlos III de Madrid. Departamento de Economía.
    13. Hidalgo, Javier & Zaffaroni, Paolo, 2007. "A goodness-of-fit test for ARCH([infinity]) models," Journal of Econometrics, Elsevier, vol. 141(2), pages 835-875, December.
    14. Linton, Oliver B. & Mammen, Enno, 2008. "Nonparametric transformation to white noise," Journal of Econometrics, Elsevier, vol. 142(1), pages 241-264, January.
    15. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.
    16. Stanislav Anatolyev, 2005. "Optimal Instruments in Time Series: A Survey," Working Papers w0069, Center for Economic and Financial Research (CEFIR).
    17. Gregory Connor & Oliver Linton & Matthias Hagmann, 2007. "Efficient Estimation of a Semiparametric Characteristic-Based Factor Model of Security Returns," FMG Discussion Papers dp599, Financial Markets Group.
    18. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    19. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2013. "A nonparametric test of a strong leverage hypothesis," CeMMAP working papers 28/13, Institute for Fiscal Studies.
    20. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    21. HAFNER, Christian & PREMINGER, Arie, 2016. "On Asymptotic Theory for ARCH(infinite) Models," LIDAM Discussion Papers CORE 2016030, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    22. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    23. Heejoon Han & Shen Zhang, 2012. "Non‐stationary non‐parametric volatility model," Econometrics Journal, Royal Economic Society, vol. 15(2), pages 204-225, June.
    24. Chen, Xiaohong & Huang, Zhuo & Yi, Yanping, 2021. "Efficient estimation of multivariate semi-nonparametric GARCH filtered copula models," Journal of Econometrics, Elsevier, vol. 222(1), pages 484-501.
    25. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
    26. Songhua Tan & Qianqian Zhu, 2022. "Asymmetric linear double autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 371-388, May.
    27. Degui Li & Oliver Linton & Zudi Lu, 2010. "Loch Linear Fitting under Near Epoch Dependence: Uniform Consistency with Convergence Rate," STICERD - Econometrics Paper Series 549, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    28. Xiaohong Chen & Zhuo Huang & Yanping Yi, 2019. "Efficient Estimation of Multivariate Semi-nonparametric GARCH Filtered Copula Models," Cowles Foundation Discussion Papers 2215, Cowles Foundation for Research in Economics, Yale University.
    29. Perron, Benoit, 2004. "Détection non paramétrique de sauts dans la volatilité des marchés financiers," L'Actualité Economique, Société Canadienne de Science Economique, vol. 80(2), pages 229-251, Juin-Sept.
    30. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    31. Hou, Ai Jun, 2013. "Asymmetry effects of shocks in Chinese stock markets volatility: A generalized additive nonparametric approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 23(C), pages 12-32.
    32. Royer, Julien, 2021. "Conditional asymmetry in Power ARCH($\infty$) models," MPRA Paper 109118, University Library of Munich, Germany.
    33. Matthieu Garcin & Clément Goulet, 2015. "Non-parameteric news impact curve: a variational approach," Documents de travail du Centre d'Economie de la Sorbonne 15086r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jul 2016.
    34. Linton, Oliver & Srisuma, Sorawoot, 2010. "Semiparametric estimation of Markov decision processeswith continuous state space," LSE Research Online Documents on Economics 58187, London School of Economics and Political Science, LSE Library.
    35. Woocheol Kim, 2004. "Identification And Estimation Of Nonparametric Structural," Econometric Society 2004 Far Eastern Meetings 733, Econometric Society.
    36. Wilson Ye Chen & Richard H. Gerlach, 2017. "Semiparametric GARCH via Bayesian model averaging," Papers 1708.07587, arXiv.org.
    37. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    38. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    39. Dahl Christian M & Iglesias Emma, 2011. "Modeling the Volatility-Return Trade-Off When Volatility May Be Nonstationary," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-32, February.
    40. Dag Tjøstheim, 2012. "Rejoinder on: Some recent theory for autoregressive count time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 469-476, September.
    41. Matthieu Garcin & Clément Goulet, 2015. "Non-parameteric news impact curve: a variational approach," Documents de travail du Centre d'Economie de la Sorbonne 15086rr, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Feb 2017.
    42. Muhammad Surajo Sanusi, 2017. "Investigating the sources of Black’s leverage effect in oil and gas stocks," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1318812-131, January.
    43. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
    44. Degiannakis, Stavros & Floros, Christos & Livada, Alexandra, 2012. "Evaluating Value-at-Risk Models before and after the Financial Crisis of 2008: International Evidence," MPRA Paper 80463, University Library of Munich, Germany.
    45. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.

  93. Arthur Lewbel & Oliver Linton, 2003. "Nonparametric Matching and Efficient Estimators of Homothetically Separable Functions," Boston College Working Papers in Economics 585, Boston College Department of Economics, revised 04 Sep 2006.

    Cited by:

    1. Nir Billfeld & Moshe Kim, 2019. "Semiparametric Wavelet-based JPEG IV Estimator for endogenously truncated data," Papers 1908.02166, arXiv.org.
    2. David Jacho-Chavez & Arthur Lewbel & Oliver Linton, 2006. "Identification and Nonparametric Estimation of a Transformed Additively Separable Model," Boston College Working Papers in Economics 652, Boston College Department of Economics, revised 26 Nov 2008.
    3. Le-Yu Chen, 2009. "Identification of structural dynamic discrete choice models," CeMMAP working papers CWP08/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    5. Daniel J. Henderson, 2009. "A Non‐parametric Examination of Capital–Skill Complementarity," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(4), pages 519-538, August.
    6. Escanciano, Juan Carlos & Jacho-Chávez, David T. & Lewbel, Arthur, 2014. "Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 426-443.
    7. Chetverikov, Denis & Wilhelm, Daniel & Kim, Dongwoo, 2021. "An Adaptive Test Of Stochastic Monotonicity," Econometric Theory, Cambridge University Press, vol. 37(3), pages 495-536, June.
    8. Delgado, Miguel A. & Escanciano, Juan Carlos, 2012. "Distribution-free tests of stochastic monotonicity," Journal of Econometrics, Elsevier, vol. 170(1), pages 68-75.
    9. Dong, Yingying & Lewbel, Arthur, 2011. "Nonparametric identification of a binary random factor in cross section data," Journal of Econometrics, Elsevier, vol. 163(2), pages 163-171, August.
    10. Centorrino, Samuele & Florens, Jean-Pierre, 2021. "Nonparametric Instrumental Variable Estimation of Binary Response Models with Continuous Endogenous Regressors," Econometrics and Statistics, Elsevier, vol. 17(C), pages 35-63.
    11. Sungwon Lee, 2020. "Nonparametric Identification and Estimation of Panel Quantile Models with Sample Selection," Working Papers 2012, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).

  94. Mototsugu Shintani & Oliver Linton, 2003. "Nonparametric Neural Network Estimation of Lyapunov Exponents and a Direct Test for Chaos," Vanderbilt University Department of Economics Working Papers 0309, Vanderbilt University Department of Economics.

    Cited by:

    1. Dominique Guegan & Justin Leroux, 2009. "Local Lyapunov Exponents: A new way to predict chaotic systems," Post-Print halshs-00511996, HAL.
    2. Park, Joon Y. & Whang, Yoon-Jae, 2012. "Random walk or chaos: A formal test on the Lyapunov exponent," Journal of Econometrics, Elsevier, vol. 169(1), pages 61-74.
    3. Matilla-García, Mariano & Marín, Manuel Ruiz, 2010. "A new test for chaos and determinism based on symbolic dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 600-614, December.
    4. Bask, Miia & Bask, Mikael, 2013. "Social Influence and the Matthew Mechanism: The Case of an Artificial Cultural Market," Working Paper Series 2013:11, Uppsala University, Department of Economics.
    5. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    6. Cars Hommes & Sebastiano Manzan, 2006. "Testing for Nonlinear Structure and Chaos in Economic Time. A Comment," Tinbergen Institute Discussion Papers 06-030/1, Tinbergen Institute.
    7. Bask, Mikael & Widerberg, Anna, 2009. "Market structure and the stability and volatility of electricity prices," Energy Economics, Elsevier, vol. 31(2), pages 278-288, March.
    8. Borusyak, K., 2011. "Nonlinear Dynamics of the Russian Stock Market in Problems of Risk Management," Journal of the New Economic Association, New Economic Association, issue 11, pages 85-105.
    9. Guégan, Dominique & Leroux, Justin, 2009. "Forecasting chaotic systems: The role of local Lyapunov exponents," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2401-2404.
    10. Dominique Guegan & Justin Leroux, 2010. "Predicting chaos with Lyapunov exponents: Zero plays no role in forecasting chaotic systems," Post-Print halshs-00462454, HAL.
    11. Bask, Mikael, 2007. "Measuring potential market risk," Bank of Finland Research Discussion Papers 20/2007, Bank of Finland.
    12. Belaire-Franch, Jorge, 2018. "Exchange rates expectations and chaotic dynamics: A replication study," Economics Discussion Papers 2018-34, Kiel Institute for the World Economy (IfW Kiel).
    13. Dominique Guegan, 2009. "Chaos in Economics and Finance," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00375713, HAL.
    14. Kyrtsou, Catherine & Serletis, Apostolos, 2006. "Univariate tests for nonlinear structure," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 154-168, March.
    15. Dominique Guegan & Justin Leroux, 2008. "Forecasting chaotic systems : the role of local Lyapunov exponents," Post-Print halshs-00259238, HAL.
    16. William Barnett, 2005. "Comment on 'Chaotic Monetary Dynamics with Confidence'," Macroeconomics 0505017, University Library of Munich, Germany.
    17. Serletis, Apostolos & He, Mingyu & Chowdhury, M.M. Islam, 2023. "Chaos in long-maturity real rates," Economics Letters, Elsevier, vol. 225(C).
    18. Bask, Mikael & Widerberg, Anna, 2007. "The Stability and Volatility of Electricity Prices: An Illustration of (lambda, sigma-2) Analysis," Working Papers in Economics 267, University of Gothenburg, Department of Economics.
    19. DIMA, Bogdan & DIMA, Ştefana Maria & IOAN, Roxana, 2021. "Remarks on the behaviour of financial market efficiency during the COVID-19 pandemic. The case of VIX," Finance Research Letters, Elsevier, vol. 43(C).
    20. Lorenzo Trapani, 2021. "Testing for strict stationarity in a random coefficient autoregressive model," Econometric Reviews, Taylor & Francis Journals, vol. 40(3), pages 220-256, April.
    21. Dominique Guegan & Justin Leroux, 2009. "Forecasting chaotic systems: The role of local Lyapunov exponents," Post-Print halshs-00431726, HAL.
    22. Shintani, Mototsugu, 2008. "A dynamic factor approach to nonlinear stability analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 32(9), pages 2788-2808, September.
    23. Dominique Guégan & Justin Leroux, 2007. "Forecasting chaotic systems: The role of local Lyapunov exponents," Cahiers de recherche 07-12, HEC Montréal, Institut d'économie appliquée.
    24. Mastroeni, Loretta & Vellucci, Pierluigi & Naldi, Maurizio, 2019. "A reappraisal of the chaotic paradigm for energy commodity prices," Energy Economics, Elsevier, vol. 82(C), pages 167-178.
    25. Sandubete, Julio E. & Escot, Lorenzo, 2020. "Chaotic signals inside some tick-by-tick financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    26. Jorge Belaire-Franch & Kwaku Opong, 2013. "A Time Series Analysis of U.K. Construction and Real Estate Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 46(3), pages 516-542, April.
    27. Elena Olmedo & Ricardo Gimeno & Lorenzo Escot & Ruth Mateos, 2007. "Convergencia y Estabilidad de los Tipos de Cambio Europeos: Una Aplicación de Exponentes de Lyapunov," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 44(129), pages 91-108.
    28. Miia Bask & Mikael Bask, 2015. "Cumulative (Dis)Advantage and the Matthew Effect in Life-Course Analysis," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    29. Serletis, Apostolos & Shintani, Mototsugu, 2006. "Chaotic monetary dynamics with confidence," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 228-252, March.
    30. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    31. Marisa Faggini, 2011. "Chaotic Time Series Analysis in Economics: Balance and Perspectives," Working papers 25, Former Department of Economics and Public Finance "G. Prato", University of Torino.
    32. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    33. Marco Pangallo, 2020. "Synchronization of endogenous business cycles," Papers 2002.06555, arXiv.org, revised Jun 2023.
    34. Dominique Guegan & Justin Leroux, 2009. "Local Lyapunov Exponents: A new way to predict chaotic systems," PSE-Ecole d'économie de Paris (Postprint) halshs-00511996, HAL.
    35. Dominique Guegan & Justin Leroux, 2009. "Forecasting chaotic systems: The role of local Lyapunov exponents," PSE-Ecole d'économie de Paris (Postprint) halshs-00431726, HAL.
    36. Charles-Cadogan, G., 2021. "Market Instability, Investor Sentiment, And Probability Judgment Error in Index Option Prices," CRETA Online Discussion Paper Series 71, Centre for Research in Economic Theory and its Applications CRETA.
    37. Marco Pangallo, 2023. "Synchronization of endogenous business cycles," LEM Papers Series 2023/01, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    38. Marisa Faggini & Bruna Bruno & Anna Parziale, 2019. "Does Chaos Matter in Financial Time Series Analysis?," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 18-24.
    39. Vitaliy Vandrovych, 2005. "Study of Nonlinearities in the Dynamics of Exchange Rates: Is There Any Evidence of Chaos?," Computing in Economics and Finance 2005 234, Society for Computational Economics.
    40. Serletis, Apostolos & Shahmoradi, Asghar, 2007. "Chaos, self-organized criticality, and SETAR nonlinearity: An analysis of purchasing power parity between Canada and the United States," Chaos, Solitons & Fractals, Elsevier, vol. 33(5), pages 1437-1444.
    41. Wolff, Rodney C. & Yao, Qiwei & Tong, Howell, 2004. "Statistical tests for Lyapunov exponents of deterministic systems," LSE Research Online Documents on Economics 154, London School of Economics and Political Science, LSE Library.
    42. Hommes, C.H. & Manzan, S., 2005. "Testing for Nonlinear Structure and Chaos in Economic Time Series: A Comment," CeNDEF Working Papers 05-14, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    43. Lucía Inglada-Pérez & Pablo Coto-Millán, 2021. "A Chaos Analysis of the Dry Bulk Shipping Market," Mathematics, MDPI, vol. 9(17), pages 1-35, August.
    44. Escot, Lorenzo & Sandubete, Julio E., 2023. "Estimating Lyapunov exponents on a noisy environment by global and local Jacobian indirect algorithms," Applied Mathematics and Computation, Elsevier, vol. 436(C).
    45. Patrick Krieger & Carsten Lausberg & Kristin Wellner, 2018. "Einblicke in die Gründe für nicht-normalverteilte Immobilienrenditen: eine explorative Untersuchung deutscher Wohnimmobilienportfolios [Insights into the reasons for non-normal real estate returns:," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 4(1), pages 49-79, November.
    46. Dominique Guégan & Justin Leroux, 2008. "Local Lyapunov exponents: Zero plays no role in Forecasting chaotic systems," Cahiers de recherche 08-10, HEC Montréal, Institut d'économie appliquée.
    47. Xu, Fei & Lai, Yongzeng & Shu, Xiao-Bao, 2018. "Chaos in integer order and fractional order financial systems and their synchronization," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 125-136.
    48. Serletis, Apostolos & Uritskaya, Olga Y., 2007. "Detecting signatures of stochastic self-organization in US money and velocity measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 281-291.
    49. Bask, Mikael & Liu, Tung & Widerberg, Anna, 2006. "The stability of electricity prices: estimation and inference of the Lyapunov exponents," Bank of Finland Research Discussion Papers 9/2006, Bank of Finland.
    50. Giannerini Simone & Rosa Rodolfo, 2004. "Assessing Chaos in Time Series: Statistical Aspects and Perspectives," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-25, May.
    51. Serletis, Apostolos & Shahmoradi, Asghar & Serletis, Demitre, 2007. "Effect of noise on estimation of Lyapunov exponents from a time series," Chaos, Solitons & Fractals, Elsevier, vol. 32(2), pages 883-887.
    52. Yankou Diasso, 2014. "Dynamique du prix international du coton : aléas, aversion au risque et chaos," Recherches économiques de Louvain, De Boeck Université, vol. 80(4), pages 53-86.
    53. BenSaïda, Ahmed & Litimi, Houda, 2013. "High level chaos in the exchange and index markets," Chaos, Solitons & Fractals, Elsevier, vol. 54(C), pages 90-95.
    54. Matilla-Garci­a, Mariano & Ruiz Mari­n, Manuel, 2008. "A non-parametric independence test using permutation entropy," Journal of Econometrics, Elsevier, vol. 144(1), pages 139-155, May.
    55. Lucía Inglada-Pérez & Sandra González y Gil, 2024. "A Study on the Nature of Complexity in the Spanish Electricity Market Using a Comprehensive Methodological Framework," Mathematics, MDPI, vol. 12(6), pages 1-21, March.

  95. Arthur Lewbel & Oliver Linton, 2003. "Nonparametric estimation of homothetic and homothetically separable functions," CeMMAP working papers CWP14/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    1. Muhammad Nazmul Khan, 2022. "Estimating empirical marginal adjustment cost function: a power series approach," Empirical Economics, Springer, vol. 63(6), pages 3185-3210, December.

  96. Wolfgang Härdle & Oliver Linton & Wang, Qihua, 2003. "Semiparametric regression analysis with missing response at random," CeMMAP working papers CWP11/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Wangli Xu & Xu Guo, 2013. "Nonparametric checks for varying coefficient models with missing response at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(4), pages 459-482, May.
    2. Bindele, Huybrechts F., 2018. "Covariates missing at random under signed-rank inference," Econometrics and Statistics, Elsevier, vol. 8(C), pages 78-93.
    3. Lai, Peng & Wang, Qihua, 2014. "Semiparametric efficient estimation for partially linear single-index models with responses missing at random," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 33-50.
    4. Zhong, Ping-Shou & Chen, Sixia, 2014. "Jackknife empirical likelihood inference with regression imputation and survey data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 193-205.
    5. Ash Abebe & Huybrechts F. Bindele & Masego Otlaadisa & Boikanyo Makubate, 2021. "Robust estimation of single index models with responses missing at random," Statistical Papers, Springer, vol. 62(5), pages 2195-2225, October.
    6. Sun, Zhihua & Wang, Qihua & Dai, Pengjie, 2009. "Model checking for partially linear models with missing responses at random," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 636-651, April.
    7. Qihua Wang & Tao Zhang & Wolfgang Karl Härdle, 2014. "An Extended Single Index Model with Missing Response at Random," SFB 649 Discussion Papers SFB649DP2014-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Levon Demirdjian & Majid Mojirsheibani, 2019. "Kernel classification with missing data and the choice of smoothing parameters," Statistical Papers, Springer, vol. 60(5), pages 1487-1513, October.
    9. Xuewen Lu & Heng Lian & Wanrong Liu, 2012. "Semiparametric estimation for inverse density weighted expectations when responses are missing at random," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(1), pages 139-152.
    10. Peixin Zhao & Xinrong Tang, 2016. "Imputation based statistical inference for partially linear quantile regression models with missing responses," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 991-1009, November.
    11. Majid Mojirsheibani & Timothy Reese, 2017. "Kernel regression estimation for incomplete data with applications," Statistical Papers, Springer, vol. 58(1), pages 185-209, March.
    12. Bianco, Ana & Boente, Graciela & González-Manteiga, Wenceslao & Pérez-González, Ana, 2010. "Estimation of the marginal location under a partially linear model with missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 546-564, February.
    13. Yan-Ting Xiao & Fu-Xiao Li, 2020. "Estimation in partially linear varying-coefficient errors-in-variables models with missing response variables," Computational Statistics, Springer, vol. 35(4), pages 1637-1658, December.
    14. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2015. "Generalised partially linear regression with misclassified data and an application to labour market transitions," ZEW Discussion Papers 15-043, ZEW - Leibniz Centre for European Economic Research.
    15. Chen, Song Xi & Van Keilegom, Ingrid, 2013. "Estimation in semiparametric models with missing data," LIDAM Reprints ISBA 2013024, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. Francesco Bravo & David Jacho-Chavez, 2011. "Empirical Likelihood for Efficient Semiparametric Average Treatment Effects," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 1-24.
    17. Zhao, Hui & Zhao, Pu-Ying & Tang, Nian-Sheng, 2013. "Empirical likelihood inference for mean functionals with nonignorably missing response data," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 101-116.
    18. Francesco Bravo, 2013. "Partially linear varying coefficient models with missing at random responses," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 721-762, August.
    19. Tang, Niansheng & Xia, Linli & Yan, Xiaodong, 2019. "Feature screening in ultrahigh-dimensional partially linear models with missing responses at random," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 208-227.
    20. Chen, Songxi, 2012. "Estimation in semiparametric models with missing data," MPRA Paper 46216, University Library of Munich, Germany.
    21. Inkmann, J., 2005. "Inverse Probability Weighted Generalised Empirical Likelihood Estimators : Firm Size and R&D Revisited," Other publications TiSEM c39cff1f-16c1-4446-a83f-c, Tilburg University, School of Economics and Management.
    22. Wangli Xu & Xu Guo & Lixing Zhu, 2012. "Goodness-of-fitting for partial linear model with missing response at random," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(1), pages 103-118.
    23. Xiaohui Liu & Zhizhong Wang & Xuemei Hu, 2011. "Testing heteroscedasticity in partially linear models with missing covariates," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 321-337.
    24. Mariela Sued & Marina Valdora & Víctor Yohai, 2020. "Robust doubly protected estimators for quantiles with missing data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 819-843, September.
    25. Wangli Xu & Lixing Zhu, 2013. "Testing the adequacy of varying coefficient models with missing responses at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 53-69, January.
    26. Lu Li & Niwen Zhou & Lixing Zhu, 2022. "Outcome regression-based estimation of conditional average treatment effect," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 987-1041, October.
    27. Bianco, Ana M. & Boente, Graciela & González-Manteiga, Wenceslao & Pérez-González, Ana, 2015. "Robust inference in partially linear models with missing responses," Statistics & Probability Letters, Elsevier, vol. 97(C), pages 88-98.
    28. Nian-Sheng Tang & Pu-Ying Zhao, 2013. "Empirical likelihood semiparametric nonlinear regression analysis for longitudinal data with responses missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 639-665, August.
    29. Yu-Ye Zou & Han-Ying Liang & Jing-Jing Zhang, 2015. "Nonlinear wavelet density estimation with data missing at random when covariates are present," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 967-995, November.
    30. Ana M. Bianco & Graciela Boente & Wenceslao González-Manteiga & Ana Pérez-González, 2019. "Plug-in marginal estimation under a general regression model with missing responses and covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 106-146, March.
    31. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    32. Nengxiang Ling & Rui Kan & Philippe Vieu & Shuyu Meng, 2019. "Semi-functional partially linear regression model with responses missing at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 39-70, January.
    33. Bindele, Huybrechts F. & Nguelifack, Brice M., 2019. "Generalized signed-rank estimation for regression models with non-ignorable missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 14-33.
    34. Bindele, Huybrechts F. & Abebe, Ash, 2015. "Semi-parametric rank regression with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 117-132.
    35. Guo, Xu & Fang, Yun & Zhu, Xuehu & Xu, Wangli & Zhu, Lixing, 2018. "Semiparametric double robust and efficient estimation for mean functionals with response missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 325-339.
    36. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    37. Wang, Qihua & Sun, Zhihua, 2007. "Estimation in partially linear models with missing responses at random," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1470-1493, August.
    38. Weiwei Zhang & Jingxuan Luo & Shengyun Ma, 2023. "Estimation in Semi-Varying Coefficient Heteroscedastic Instrumental Variable Models with Missing Responses," Mathematics, MDPI, vol. 11(23), pages 1-20, December.
    39. Qihua Wang & Gregg Dinse & Chunling Liu, 2012. "Hazard function estimation with cause-of-death data missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 415-438, April.
    40. Nengxiang Ling & Lilei Cheng & Philippe Vieu & Hui Ding, 2022. "Missing responses at random in functional single index model for time series data," Statistical Papers, Springer, vol. 63(2), pages 665-692, April.
    41. Ana Bianco & Graciela Boente & Wenceslao González-Manteiga & Ana Pérez-González, 2011. "Asymptotic behavior of robust estimators in partially linear models with missing responses: the effect of estimating the missing probability on the simplified marginal estimators," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 524-548, November.
    42. Xue, Liugen & Zhang, Jinghua, 2020. "Empirical likelihood for partially linear single-index models with missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    43. Chen, Xiaohong & Hong, Han & Tarozzi, Alessandro, 2008. "Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects," Working Papers 42, Yale University, Department of Economics.
    44. Shuanghua Luo & Changlin Mei & Cheng-yi Zhang, 2017. "Smoothed empirical likelihood for quantile regression models with response data missing at random," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(1), pages 95-116, January.
    45. Mojirsheibani, Majid & Montazeri, Zahra, 2007. "On nonparametric classification with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 1051-1071, May.
    46. Yu Shen & Han-Ying Liang, 2018. "Quantile regression and its empirical likelihood with missing response at random," Statistical Papers, Springer, vol. 59(2), pages 685-707, June.
    47. Zhangong Zhou & Linjun Tang, 2019. "Testing for parametric component of partially linear models with missing covariates," Statistical Papers, Springer, vol. 60(3), pages 747-760, June.
    48. A. Pérez-González & J. Vilar-Fernández & W. González-Manteiga, 2009. "Asymptotic properties of local polynomial regression with missing data and correlated errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 85-109, March.
    49. M. Hristache & V. Patilea, 2017. "Conditional moment models with data missing at random," Biometrika, Biometrika Trust, vol. 104(3), pages 735-742.
    50. Guo, Xu & Wang, Tao & Xu, Wangli & Zhu, Lixing, 2014. "Dimension reduction with missing response at random," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 228-242.
    51. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 1053-1079.
    52. Tianfa Xie & Zhihua Sun & Liuquan Sun, 2012. "A consistent model specification test for a partial linear model with covariates missing at random," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 841-856, December.
    53. Wang, Zhaoliang & Xue, Liugen & Liu, Juanfang, 2019. "Checking nonparametric component for partially nonlinear model with missing response," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 1-8.
    54. Hu, Yanan & Yang, Yaqi & Wang, Chunyu & Tian, Maozai, 2017. "Imputation in nonparametric quantile regression with complex data," Statistics & Probability Letters, Elsevier, vol. 127(C), pages 120-130.
    55. Liang, Hua & Su, Haiyan & Zou, Guohua, 2008. "Confidence intervals for a common mean with missing data with applications in an AIDS study," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 546-553, December.
    56. Yongsong Qin & Jianjun Li, 2011. "Empirical likelihood for partially linear models with missing responses at random," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 497-511.
    57. Shuanghua Luo & Cheng-yi Zhang, 2016. "Nonparametric $$M$$ M -type regression estimation under missing response data," Statistical Papers, Springer, vol. 57(3), pages 641-664, September.
    58. Qi-Hua Wang, 2009. "Statistical estimation in partial linear models with covariate data missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 47-84, March.
    59. Lee, Jiyon, 2015. "A semiparametric single index model with heterogeneous impacts on an unobserved variable," Journal of Econometrics, Elsevier, vol. 184(1), pages 13-36.
    60. Xue, Liugen & Xue, Dong, 2011. "Empirical likelihood for semiparametric regression model with missing response data," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 723-740, April.
    61. Lai, Peng & Liu, Yiming & Liu, Zhi & Wan, Yi, 2017. "Model free feature screening for ultrahigh dimensional data with responses missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 201-216.

  97. Linton, Oliver & Maasoumi, Esfandiar & Whang, Yoon-Jae, 2003. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," SFB 373 Discussion Papers 2003,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers CWP24/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Chuan-Hao Hsu & Hung-Gay Fung & Yi-Ping Chang, 2016. "The performance of Taiwanese firms after a share repurchase announcement," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1251-1269, November.
    3. Maasoumi, Esfandiar & Eren, Ozkan, 2006. "The Information Basis of Matching with Propensity Score," Departmental Working Papers 0606, Southern Methodist University, Department of Economics.
    4. Zhuo Qiao & Ephraim Clark & Wing-Keung Wong, 2014. "Investors’ preference towards risk: evidence from the Taiwan stock and stock index futures markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 54(1), pages 251-274, March.
    5. Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & Maasoumi, Esfandiar & McAleer, Michael & Pérez-Amaral, Teodosio, 2019. "Choosing expected shortfall over VaR in Basel III using stochastic dominance," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 95-113.
    6. Vivek Dehejia & Marcel Voia, 2008. "International Income Comparisons and Location Choice: Methodology, Analysis, and Implications," Carleton Economic Papers 08-02, Carleton University, Department of Economics.
    7. Fong, Wai Mun, 2016. "Stochastic dominance and the omega ratio," Finance Research Letters, Elsevier, vol. 17(C), pages 7-9.
    8. Tukiainen, Janne, 2008. "Testing for common costs in the City of Helsinki bus transit auctions," International Journal of Industrial Organization, Elsevier, vol. 26(6), pages 1308-1322, November.
    9. Anissa Chaibi & Maria-Lenuta Ciupac-Ulici & Mircea-Cristian Gherman, 2014. "Do Recent Stochastic Tools Help to Better Understand Investors Preference and Asset Allocation?," Working Papers 2014-130, Department of Research, Ipag Business School.
    10. Esfandiar Maasoumi & Daniel L. Millimet & Dipanwita Sarkar, 2009. "Who Benefits from Marriage?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 1-33, February.
    11. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers 24/12, Institute for Fiscal Studies.
    12. Linton, Oliver & Seo, Myung Hwan & Whang, Yoon-Jae, 2023. "Testing stochastic dominance with many conditioning variables," Journal of Econometrics, Elsevier, vol. 235(2), pages 507-527.
    13. Maasoumi, Esfandiar & Racine, Jeff & Stengos, Thanasis, 2007. "Growth and convergence: A profile of distribution dynamics and mobility," Journal of Econometrics, Elsevier, vol. 136(2), pages 483-508, February.
    14. Elettra Agliardi & Mehmet Pinar & Thanasis Stengos, 2015. "An environmental degradation index based on stochastic dominance," Empirical Economics, Springer, vol. 48(1), pages 439-459, February.
    15. Park, Joon Y., 2005. "The Spatial Analysis of Time Series," Working Papers 2005-07, Rice University, Department of Economics.
    16. Arvanitis, Stelios & Post, Thierry & Potì, Valerio & Karabati, Selcuk, 2021. "Nonparametric tests for Optimal Predictive Ability," International Journal of Forecasting, Elsevier, vol. 37(2), pages 881-898.
    17. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "Make Almost Stochastic Dominance really Almost," MPRA Paper 49745, University Library of Munich, Germany.
    18. Arvanitis, Stelios & Scaillet, Olivier & Topaloglou, Nikolas, 2020. "Spanning tests for Markowitz stochastic dominance," Journal of Econometrics, Elsevier, vol. 217(2), pages 291-311.
    19. Esfandiar Maasoumi & M. Melinda Pitts & Ke Wu, 2014. "The gap between the conditional wage distributions of incumbents and the newly hired employees: decomposition and uniform ordering," FRB Atlanta Working Paper 2014-22, Federal Reserve Bank of Atlanta.
    20. Vance Martin & G.C. Lim & Esfandiar Maasoumi, 2004. "Discounting The Equity Premium Puzzle," Econometric Society 2004 Australasian Meetings 331, Econometric Society.
    21. Oliver Linton & Katja Smetanina, 2015. "Mean Ratio Statistic for measuring predictability," CeMMAP working papers 08/15, Institute for Fiscal Studies.
    22. Canepa, Alessandra & de la O. González, María & Skinner, Frank S., 2019. "Hedge Fund Strategies: A non-Parametric Analysis," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201902, University of Turin.
    23. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    24. Lean, H.H. & McAleer, M.J. & Wong, W.-K., 2013. "Risk-averse and Risk-seeking Investor Preferences for Oil Spot and Futures," Econometric Institute Research Papers EI 2013-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    25. Lean, Hooi-Hooi & Wong, Wing-Keung & Zhang, Xibin, 2008. "The sizes and powers of some stochastic dominance tests: A Monte Carlo study for correlated and heteroskedastic distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(1), pages 30-48.
    26. Wen-Hao Chen & Jean-Yves Duclos, 2009. "Testing for poverty dominance: an application to Canada," Working Papers 379, Barcelona School of Economics.
    27. Kim Huynh & David Jacho-Chávez & Robert Petrunia & Marcel Voia, 2015. "A nonparametric analysis of firm size, leverage and labour productivity distribution dynamics," Empirical Economics, Springer, vol. 48(1), pages 337-360, February.
    28. Härdle, Wolfgang Karl & Schulz, Rainer & Xie, Taojun, 2019. "Cooling Measures and Housing Wealth: Evidence from Singapore," IRTG 1792 Discussion Papers 2019-001, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    29. Thomas C. Chiang & Hooi Hooi Lean & Wing-Keung Wong, 2008. "Do REITs Outperform Stocks and Fixed-Income Assets? New Evidence from Mean-Variance and Stochastic Dominance Approaches," JRFM, MDPI, vol. 1(1), pages 1-40, December.
    30. Bogomolov, Marina & Davidov, Ori, 2019. "Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 20-27.
    31. Kai-Yin Woo & Chulin Mai & Michael McAleer & Wing-Keung Wong, 2020. "Review on Efficiency and Anomalies in Stock Markets," Economies, MDPI, vol. 8(1), pages 1-51, March.
    32. Chang, Chia-Lin & Jiménez-Martín, Juan-Ángel & Maasoumi, Esfandiar & Pérez-Amaral, Teodosio, 2015. "A stochastic dominance approach to financial risk management strategies," Journal of Econometrics, Elsevier, vol. 187(2), pages 472-485.
    33. Post, Thierry & Kopa, Miloš, 2013. "Aggregate investor preferences and beliefs: A comment," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 187-190.
    34. Hooi Hooi Lean & Michael McAleer & Wing-Keung Wong, 2010. "Investor Preferences for Oil Spot and Futures Based on Mean-Variance and Stochastic Dominance," Working Papers in Economics 10/22, University of Canterbury, Department of Economics and Finance.
    35. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Identification of Panel Data Models with Endogenous Censoring," MPRA Paper 30373, University Library of Munich, Germany.
    36. Almas Heshmati & Esfandiar Maasoumi & Guanghua Wan, 2019. "An Analysis of the Determinants of Household Consumption Expenditure and Poverty in India," Economies, MDPI, vol. 7(4), pages 1-27, September.
    37. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2014. "Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting," Working Papers 201404, University of California at Riverside, Department of Economics.
    38. Andrey Lizyayev, 2012. "Stochastic dominance efficiency analysis of diversified portfolios: classification, comparison and refinements," Annals of Operations Research, Springer, vol. 196(1), pages 391-410, July.
    39. Linton, Oliver, 2005. "Nonparametric Inference For Unbalanced Time Series Data," Econometric Theory, Cambridge University Press, vol. 21(1), pages 143-157, February.
    40. Canepa, Alessandra & Chersoni, Giulia & Fontana, Magda, 2023. "The role of environmental and financial motivations in the adoption of energy-saving technologies: Evidence from European Union data," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 1-14.
    41. Stelios Arvanitis & Thierry Post, 2024. "Stochastic Arbitrage Opportunities: Set Estimation and Statistical Testing," Mathematics, MDPI, vol. 12(4), pages 1-19, February.
    42. Jingfang Zhang & Emir Malikov, 2023. "Detecting Learning by Exporting and from Exporters," Papers 2302.13427, arXiv.org.
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    184. Canepa, Alessandra & Fontana, Magda & Chersoni, Giulia, 2021. "The Role of Environmental and Financial Concerns on Energy-Saving Investments: A Stochastic Dominance Analysis," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202109, University of Turin.
    185. Oliver Linton & Yoon-Jae Whang, 2012. "Testing for the stochastic dominance efficiency of a given portfolio," CeMMAP working papers 27/12, Institute for Fiscal Studies.
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    212. Brendan Kline, 2016. "Identification of the Direction of a Causal Effect by Instrumental Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 176-184, April.
    213. Agliardi, Elettra & Agliardi, Rossella & Pinar, Mehmet & Stengos, Thanasis & Topaloglou, Nikolas, 2012. "A new country risk index for emerging markets: A stochastic dominance approach," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 741-761.
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    215. David Lander & David Gunawan & William E. Griffiths & Duangkamon Chotikapanich, 2016. "Bayesian Assessment of Lorenz and Stochastic Dominance Using a Mixture of Gamma Densities," Department of Economics - Working Papers Series 2023, The University of Melbourne.
    216. Levy, Haim & Levy, Moshe, 2021. "Stocks versus bonds for the long run when a riskless asset is available," Journal of Banking & Finance, Elsevier, vol. 133(C).
    217. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    218. Qiao, Zhuo & Wong, Wing-Keung & Fung, Joseph K.W., 2013. "Stochastic dominance relationships between stock and stock index futures markets: International evidence," Economic Modelling, Elsevier, vol. 33(C), pages 552-559.
    219. Sungro Lee, Chang Sik Kim, In-Moo Kim & Chang Sik Kim & In-Moo Kim, 2012. "Testing the Monday Effect using High-frequency Intraday Returns: A Spatial Dominance Approach," Korean Economic Review, Korean Economic Association, vol. 28, pages 69-90.
    220. Firmin Doko Tchatoka & Robert Garrard & Virginie Masson, 2017. "Testing for Stochastic Dominance in Social Networks," School of Economics and Public Policy Working Papers 2017-02, University of Adelaide, School of Economics and Public Policy.
    221. Kapalczynski, Anna & Lien, Donald, 2021. "Effectiveness of Augmented Dollar-Cost Averaging," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    222. Fathi Abid & Pui Lam Leung & Mourad Mroua & Wing Keung Wong, 2014. "International Diversification Versus Domestic Diversification: Mean-Variance Portfolio Optimization and Stochastic Dominance Approaches," JRFM, MDPI, vol. 7(2), pages 1-22, May.
    223. Emmanuel Olateju Oyatoye & Waheed Oladimeji Arilesere, 2012. "A non-linear programming model for insurance company investment portfolio management in Nigeria," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 83-100.
    224. Garry F. Barrett & Stephen G. Donald & Yu-Chin Hsu, 2015. "Consistent Tests for Poverty Dominance Relations," IEAS Working Paper : academic research 15-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    225. Paul Makdissi & Walid Marrouch & Myra Yazbeck, 2023. "Monitoring poverty in a data deprived environment: The case of Lebanon," Working Papers 2302E Classification- I31, University of Ottawa, Department of Economics.
    226. Deborah Kim, 2020. "On the Size Control of the Hybrid Test for Predictive Ability," Papers 2008.02318, arXiv.org, revised Sep 2021.
    227. Hooi Lean & Kok Phoon & Wing-Keung Wong, 2013. "Stochastic dominance analysis of CTA funds," Review of Quantitative Finance and Accounting, Springer, vol. 40(1), pages 155-170, January.
    228. Chang, Hao-Wen & Lin, Chinho, 2023. "Currency portfolio behavior in seven major Asian markets," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 540-559.
    229. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    230. Duangkamon Chotikapanich & William E. Griffiths, 2006. "Bayesian Assessment of Lorenz and Stochastic Dominance in Income Distributions," Department of Economics - Working Papers Series 960, The University of Melbourne.
    231. Millimet Daniel L & Wang Le, 2006. "A Distributional Analysis of the Gender Earnings Gap in Urban China," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 5(1), pages 1-50, February.
    232. Quaye, Enoch & Tunaru, Radu, 2022. "The stock implied volatility and the implied dividend volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    233. Post, Thierry & Kopa, Miloš, 2013. "General linear formulations of stochastic dominance criteria," European Journal of Operational Research, Elsevier, vol. 230(2), pages 321-332.
    234. Chuang, O-Chia & Kuan, Chung-Ming & Tzeng, Larry Y., 2017. "Testing for central dominance: Method and application," Journal of Econometrics, Elsevier, vol. 196(2), pages 368-378.
    235. Annaert, Jan & Osselaer, Sofieke Van & Verstraete, Bert, 2009. "Performance evaluation of portfolio insurance strategies using stochastic dominance criteria," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 272-280, February.
    236. Chan, Chia-Ying & de Peretti, Christian & Qiao, Zhuo & Wong, Wing-Keung, 2012. "Empirical test of the efficiency of the UK covered warrants market: Stochastic dominance and likelihood ratio test approach," Journal of Empirical Finance, Elsevier, vol. 19(1), pages 162-174.
    237. Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2018. "Portfolio optimization based on stochastic dominance and empirical likelihood," Journal of Econometrics, Elsevier, vol. 206(1), pages 167-186.
    238. Al-Khazali, Osamah & Lean, Hooi Hooi & Samet, Anis, 2014. "Do Islamic stock indexes outperform conventional stock indexes? A stochastic dominance approach," Pacific-Basin Finance Journal, Elsevier, vol. 28(C), pages 29-46.
    239. Mariusz Górajski & Zbigniew Kuchta, 2022. "Which hallmarks of optimal monetary policy rules matter in Poland? A stochastic dominance approach," Bank i Kredyt, Narodowy Bank Polski, vol. 53(2), pages 149-182.
    240. Kolokolova, Olga & Le Courtois, Olivier & Xu, Xia, 2022. "Is the index efficient? A worldwide tour with stochastic dominance," Journal of Financial Markets, Elsevier, vol. 59(PB).
    241. Marcel Voia & Liqun Wang & Ricardas Zitikis, 2009. "A Distributional Analysis of Treatment Effects on Subpopulations of a Socioeconomic Experiment," Carleton Economic Papers 09-02, Carleton University, Department of Economics, revised 05 Feb 2010.
    242. Pinar, Mehmet & Stengos, Thanasis & Topaloglou, Nikolas, 2020. "On the construction of a feasible range of multidimensional poverty under benchmark weight uncertainty," European Journal of Operational Research, Elsevier, vol. 281(2), pages 415-427.
    243. Tabri, Rami V., 2015. "Empirical Likelihood for Robust Poverty Comparisons," Working Papers 2015-02, University of Sydney, School of Economics, revised May 2015.
    244. Stelios Arvanitis & Thierry Post & Nikolas Topaloglou, 2021. "Stochastic Bounds for Reference Sets in Portfolio Analysis," Management Science, INFORMS, vol. 67(12), pages 7737-7754, December.
    245. Charles Beach, 2023. "Quantile Tool Box Measures for Empirical Analysis and for Testing Distributional Comparisons in Direct Distribution-Free Fashion," Working Paper 1508, Economics Department, Queen's University.
    246. Kyungchul Song, 2009. "Testing Predictive Ability and Power Robustification," PIER Working Paper Archive 09-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    247. Brian McCaig & Adonis Yatchew, 2007. "International welfare comparisons and nonparametric testing of multivariate stochastic dominance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 951-969.
    248. Wang, Ming-Hui & Ke, Mei-Chu & Liang Liao, Tung & Chiang, Yi-Chein & Hsu, Chuan-Hao, 2020. "Alternative estimation method of earnings growth rate for PEGR strategy," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    249. Clark, Ephraim & Qiao, Zhuo, 2020. "The value premium puzzle, behavior versus risk: New evidence from China," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 12-21.
    250. Gordon Anderson & Thierry Post, 2018. "Increasing discriminatory power in well-being analysis using convex stochastic dominance," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 51(3), pages 551-561, October.
    251. Francesco Andreoli, 2018. "Robust Inference for Inverse Stochastic Dominance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 146-159, January.
    252. J. Annaert & S. Van Osselaer & B. Verstraete, 2007. "Performance evaluation of portfolio insurance strategies using stochastic dominance criteria," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/473, Ghent University, Faculty of Economics and Business Administration.
    253. Yen, Yu-Min & Yen, Tso-Jung, 2021. "Testing forecast accuracy of expectiles and quantiles with the extremal consistent loss functions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 733-758.
    254. Fang, Yi, 2012. "Aggregate investor preferences and beliefs in stock market: A stochastic dominance analysis," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 528-547.
    255. Oliver Linton, 2004. "Nonparametric inference for unbalance time series data," CeMMAP working papers 06/04, Institute for Fiscal Studies.
    256. Dehejia Vivek H. & Voia Marcel C., 2012. "International Income Comparisons and Social Welfare: Methodology, Analysis, and Implications," Journal of Globalization and Development, De Gruyter, vol. 3(1), pages 1-24, June.
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  98. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2002. "Consistent Testing for Stochastic Dominance: A Subsampling Approach," FMG Discussion Papers dp407, Financial Markets Group.

    Cited by:

    1. Maasoumi, Esfandiar & Racine, Jeff & Stengos, Thanasis, 2007. "Growth and convergence: A profile of distribution dynamics and mobility," Journal of Econometrics, Elsevier, vol. 136(2), pages 483-508, February.
    2. Park, Joon Y., 2005. "The Spatial Analysis of Time Series," Working Papers 2005-07, Rice University, Department of Economics.
    3. Wen-Hao Chen & Jean-Yves Duclos, 2009. "Testing for poverty dominance: an application to Canada," Working Papers 379, Barcelona School of Economics.
    4. Gonzalo, J. & Olmo, J., 2008. "Testing Downside Risk Efficiency Under Market Distress," Working Papers 08/11, Department of Economics, City University London.
    5. Millimet, Daniel & Wang, Le, 2005. "Is the Quantity-Quality Trade-off Really a Trade-off for All?," Departmental Working Papers 0502, Southern Methodist University, Department of Economics.
    6. George Milunovich & Susan Thorp, 2005. "Valuing Volatility Spillovers," International Finance 0506008, University Library of Munich, Germany.
    7. Néstor Gandelman, 2005. "Community tax evasion models: A stochastic dominance test," Journal of Applied Economics, Universidad del CEMA, vol. 8, pages 279-297, November.
    8. Gordon Anderson, 2008. "The empirical assessment of multidimensional welfare, inequality and poverty: Sample weighted multivariate generalizations of the Kolmogorov–Smirnov two sample tests for stochastic dominance," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(1), pages 73-87, March.
    9. Ozkan Eren & Daniel Millimet, 2007. "Time to learn? The organizational structure of schools and student achievement," Empirical Economics, Springer, vol. 32(2), pages 301-332, May.
    10. Dominic Gasbarro & Wing-Keung Wong & J. Kenton Zumwalt, 2007. "Stochastic Dominance Analysis of iShares," The European Journal of Finance, Taylor & Francis Journals, vol. 13(1), pages 89-101.
    11. Maasoumi, Esfandiar & Millimet, Daniel & Sarkar, Dipanwita, 2005. "The Distribution of Returns to Marriage," Departmental Working Papers 0503, Southern Methodist University, Department of Economics.
    12. Suhejla Hoiti & Esfandiar Maasoumi & Michael McAleer & Daniel Slottje, 2005. "Measuring the Volatility in U.S. Treasury Benchmarks and Debt Instruments," DEA Working Papers 14, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    13. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.

  99. Linton, Oliver & Whang, Yoon-Jae, 2002. "Nonparametric estimation with aggregated data," LSE Research Online Documents on Economics 320, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Delaigle, Aurore & Meister, Alexander, 2007. "Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1416-1426, December.
    2. Joel L. Horowitz, 2013. "Ill-posed inverse problems in economics," CeMMAP working papers CWP37/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Meister, Alexander, 2007. "Optimal convergence rates for density estimation from grouped data," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1091-1097, June.
    4. Carroll, Raymond J. & Delaigle, Aurore & Hall, Peter, 2009. "Nonparametric Prediction in Measurement Error Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 993-1003.
    5. Li, Tong & Hsiao, Cheng, 2004. "Robust estimation of generalized linear models with measurement errors," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 51-65.
    6. Felt, Marie-Hélène, 2020. "On the identification of joint distributions using marginals and aggregates," Economics Letters, Elsevier, vol. 194(C).
    7. Raymond J. Carroll & Aurore Delaigle & Peter Hall, 2007. "Non‐parametric regression estimation from data contaminated by a mixture of Berkson and classical errors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 859-878, November.
    8. Xiaohong Chen & Yingyao Hu, 2006. "Identification and Inference of Nonlinear Models Using Two Samples with Arbitrary Measurement Errors," Cowles Foundation Discussion Papers 1590, Cowles Foundation for Research in Economics, Yale University.
    9. Joel L. Horowitz, 2013. "Ill-posed inverse problems in economics," CeMMAP working papers 37/13, Institute for Fiscal Studies.
    10. Phuong, Cao Xuan & Thuy, Le Thi Hong, 2019. "Density deconvolution from grouped data with additive errors," Statistics & Probability Letters, Elsevier, vol. 148(C), pages 74-81.
    11. Marie-Hélène Felt, 2018. "A Look Inside the Box: Combining Aggregate and Marginal Distributions to Identify Joint Distributions," Staff Working Papers 18-29, Bank of Canada.

  100. Zhijie Xiao & Oliver Linton & Raymond J. Carroll & E. Mammen, 2002. "More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors," Cowles Foundation Discussion Papers 1375, Cowles Foundation for Research in Economics, Yale University.

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    1. Dabo-Niang, Sophie & Francq, Christian & Zakoian, Jean-Michel, 2009. "Combining parametric and nonparametric approaches for more efficient time series prediction," MPRA Paper 16893, University Library of Munich, Germany.
    2. Sophie DABO-NIANG & Christian FRANCQ & Jean-Michel ZAKOIAN, 2009. "Combining Nonparametric and Optimal Linear Time Series Predictions," Working Papers 2009-18, Center for Research in Economics and Statistics.

  101. Xiaohong Chen & Oliver Linton & Ingred van Keilegom, 2002. "Estimation of semiparametric models when the criterion function is not smooth," CeMMAP working papers CWP02/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Saraswata Chaudhuriy & David T. Frazierz & Eric Renault, 2016. "Indirect Inference with Endogenously Missing Exogenous Variables," CIRANO Working Papers 2016s-15, CIRANO.
    2. Galvao, Antonio F. & Montes-Rojas, Gabriel, 2015. "On the equivalence of instrumental variables estimators for linear models," Economics Letters, Elsevier, vol. 134(C), pages 13-15.
    3. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    4. Horowitz, Joel L. & Lee, Sokbae, 2009. "Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative," Journal of Econometrics, Elsevier, vol. 152(2), pages 141-152, October.
    5. Tae-Hwan Kim & Christophe Muller, 2020. "Inconsistency transmission and variance reduction in two-stage quantile regression," Post-Print hal-02084505, HAL.
    6. Xiaohong Chen & Demian Pouzo, 2009. "Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals," CeMMAP working papers CWP20/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Kyoo il Kim, 2006. "Set Inference for Semiparametric Discrete Games," Labor Economics Working Papers 22454, East Asian Bureau of Economic Research.
    8. Ai, Chunrong & Chen, Xiaohong, 2007. "Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables," Journal of Econometrics, Elsevier, vol. 141(1), pages 5-43, November.
    9. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    10. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    11. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
    12. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    13. Dong, Yingying, 2010. "Endogenous regressor binary choice models without instruments, with an application to migration," Economics Letters, Elsevier, vol. 107(1), pages 33-35, April.
    14. de Castro, Luciano & Galvao, Antonio F. & Noussair, Charles N. & Qiao, Liang, 2022. "Do people maximize quantiles?," Games and Economic Behavior, Elsevier, vol. 132(C), pages 22-40.
    15. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    16. Dennis Kristensen & Bernard Salanie, 2013. "Higher-order properties of approximate estimators," CeMMAP working papers 45/13, Institute for Fiscal Studies.
    17. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    18. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
    19. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Le-Yu Chen & Sokbae (Simon) Lee & Myung Jae Sung, 2014. "Maximum score estimation with nonparametrically generated regressors," CeMMAP working papers CWP27/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers CWP09/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Dennis Kristensen, 2009. "Semiparametric modelling and estimation (in Russian)," Quantile, Quantile, issue 7, pages 53-83, September.
    23. Clément de Chaisemartin, 2012. "Fuzzy differences in differences," PSE Working Papers halshs-00671368, HAL.
    24. Xiaohong Chen & Demian Pouzo, 2012. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Econometrica, Econometric Society, vol. 80(1), pages 277-321, January.
    25. Braekers, Roel & Van Keilegom, Ingrid, 2009. "Flexible modeling based on copulas in nonparametric median regression," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1270-1281, July.
    26. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    27. Chen, Xiaohong & Liao, Zhipeng, 2014. "Sieve M inference on irregular parameters," Journal of Econometrics, Elsevier, vol. 182(1), pages 70-86.
    28. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org.
    29. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2014. "Fuzzy Changes-in-Changes," Working Papers 2014-18, Center for Research in Economics and Statistics.
    30. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    31. Kristensen, Dennis, 2010. "Pseudo-maximum likelihood estimation in two classes of semiparametric diffusion models," Journal of Econometrics, Elsevier, vol. 156(2), pages 239-259, June.
    32. Xiaohong Chen & Yin Jia Jeff Qiu, 2016. "Methods for Nonparametric and Semiparametric Regressions with Endogeneity: A Gentle Guide," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 259-290, October.
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    67. Qiang Guo & Christopher Koch & Aiyong Zhu, 2017. "Joint audit, audit market structure, and consumer surplus," Review of Accounting Studies, Springer, vol. 22(4), pages 1595-1627, December.
    68. Holmes, Thomas J. & Sieg, Holger, 2015. "Structural Estimation in Urban Economics," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 69-114, Elsevier.
    69. Allen Klaiber, H. & Phaneuf, Daniel J., 2010. "Valuing open space in a residential sorting model of the Twin Cities," Journal of Environmental Economics and Management, Elsevier, vol. 60(2), pages 57-77, September.
    70. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    71. Iaria, Alessandro & ,, 2020. "Identification and Estimation of Demand for Bundles," CEPR Discussion Papers 14363, C.E.P.R. Discussion Papers.
    72. Fan, Qin & Klaiber, H. Allen & Fisher-Vanden, Karen, 2012. "Climate Change Impacts on U.S. Migration and Household Location Choice," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124588, Agricultural and Applied Economics Association.
    73. Fan, Qin & Davlasheridze, Meri, 2014. "Evaluating the Effectiveness of Flood Mitigation Policies in the U.S," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169399, Agricultural and Applied Economics Association.
    74. Evans, Keith Shannon, 2011. "Problems of uncertainty, learning, and welfare measurement in resource and environmental economics," ISU General Staff Papers 201101010800001072, Iowa State University, Department of Economics.
    75. Hyungsik Roger Roger Moon & Matthew Shum & Martin Weidner, 2014. "Estimation of random coefficients logit demand models with interactive fixed effects," CeMMAP working papers 20/14, Institute for Fiscal Studies.
    76. Bayer, Patrick & Timmins, Christopher, 2003. "Estimating Equilibrium Models of Sorting Across Locations," Center Discussion Papers 28448, Yale University, Economic Growth Center.
    77. Fernández-Antolín, Anna & Guevara, C. Angelo & de Lapparent, Matthieu & Bierlaire, Michel, 2016. "Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data," Journal of choice modelling, Elsevier, vol. 20(C), pages 1-15.
    78. Sinha, Paramita & Caulkins, Martha & Cropper, Maureen, 2021. "The value of climate amenities: A comparison of hedonic and discrete choice approaches," Journal of Urban Economics, Elsevier, vol. 126(C).
    79. Kevin YC Chung & Timothy P. Derdenger & Kannan Srinivasan, 2013. "Economic Value of Celebrity Endorsements: Tiger Woods' Impact on Sales of Nike Golf Balls," Marketing Science, INFORMS, vol. 32(2), pages 271-293, March.
    80. Sun, Yutec & Ishihara, Masakazu, 2019. "A computationally efficient fixed point approach to dynamic structural demand estimation," Journal of Econometrics, Elsevier, vol. 208(2), pages 563-584.
    81. Doi, Naoshi, 2022. "A simple method to estimate discrete-type random coefficients logit models," International Journal of Industrial Organization, Elsevier, vol. 81(C).

  103. Linton, Oliver, 2001. "Estimating additive nonparametric models by partial Lq norm: the curse of fractionality," LSE Research Online Documents on Economics 319, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    2. Kong, Efang & Linton, Oliver & Xia, Yingcun, 2010. "Uniform Bahadur Representation For Local Polynomial Estimates Of M-Regression And Its Application To The Additive Model," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1529-1564, October.
    3. Efang Kong & Oliver Linton & Yingcun Xia, 2011. "Global Bahadur representation for nonparametric censored regression quantiles and its applications," CeMMAP working papers CWP33/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  104. Andrew Jeffrey & Oliver Linton & Thong Nguyen, 2001. "Flexible Term Structure Estimation: Which Method Is Preferred?," Yale School of Management Working Papers ysm171, Yale School of Management, revised 01 Oct 2001.

    Cited by:

    1. David Bolder & Scott Gusba, 2002. "Exponentials, Polynomials, and Fourier Series: More Yield Curve Modelling at the Bank of Canada," Staff Working Papers 02-29, Bank of Canada.
    2. Clive G. Bowsher & Roland Meeks, 2008. "The dynamics of economics functions: modelling and forecasting the yield curve," Working Papers 0804, Federal Reserve Bank of Dallas.
    3. Yan Liu & Jing Cynthia Wu, 2020. "Reconstructing the Yield Curve," NBER Working Papers 27266, National Bureau of Economic Research, Inc.
    4. Antonio Diaz & Francisco Jareno & Eliseo Navarro, 2010. "Term structure of volatilities and yield curve estimation methodology," Quantitative Finance, Taylor & Francis Journals, vol. 11(4), pages 573-586.
    5. Tong, Xiaojun & He, Zhuoqiong Chong & Sun, Dongchu, 2018. "Estimating Chinese Treasury yield curves with Bayesian smoothing splines," Econometrics and Statistics, Elsevier, vol. 8(C), pages 94-124.
    6. Oliveira, Luís & Curto, José Dias & Nunes, João Pedro, 2012. "The determinants of sovereign credit spread changes in the Euro-zone," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(2), pages 278-304.
    7. Luís Oliveira & João Vidal Nunes & Luís Malcato, 2014. "The performance of deterministic and stochastic interest rate risk measures:," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 13(3), pages 141-165, December.
    8. Tatyana Krivobokova & Göran Kauermann & Theofanis Archontakis, 2006. "Estimating the term structure of interest rates using penalized splines," Statistical Papers, Springer, vol. 47(3), pages 443-459, June.
    9. Michiel De Pooter, 2007. "Examining the Nelson-Siegel Class of Term Structure Models," Tinbergen Institute Discussion Papers 07-043/4, Tinbergen Institute.

  105. Linton, Oliver Bruce & Xiao, Zhijie, 2001. "A nonparametric regression estimator that adapts to error distribution of unknown form," SFB 373 Discussion Papers 2001,33, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Wang, Dong, 2010. "Modeling epigenetic modifications under multiple treatment conditions," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1179-1189, April.
    2. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
    3. Wang, Qin & Yao, Weixin, 2012. "An adaptive estimation of MAVE," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 88-100, February.
    4. Yao, Weixin, 2013. "A note on EM algorithm for mixture models," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 519-526.
    5. Zhang, Xibin & King, Maxwell L. & Shang, Han Lin, 2014. "A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 218-234.
    6. McCloud, Nadine & Parmeter, Christopher F., 2020. "Determining the Number of Effective Parameters in Kernel Density Estimation," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    7. Moradi Rekabdarkolaee, Hossein & Wang, Qin, 2017. "Variable selection through adaptive MAVE," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 44-51.
    8. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2011. "Bayesian estimation of bandwidths for a nonparametric regression model with a flexible error density," Monash Econometrics and Business Statistics Working Papers 10/11, Monash University, Department of Econometrics and Business Statistics.
    9. Chen, Yixin & Wang, Qin & Yao, Weixin, 2015. "Adaptive estimation for varying coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 17-31.
    10. Chaouch, Mohamed, 2019. "Volatility estimation in a nonlinear heteroscedastic functional regression model with martingale difference errors," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 129-148.
    11. De Gooijer, Jan G. & Reichardt, Hugo, 2021. "A multi-step kernel–based regression estimator that adapts to error distributions of unknown form," LSE Research Online Documents on Economics 115083, London School of Economics and Political Science, LSE Library.

  106. Arthur Lewbel & Oliver Linton & Daniel McFadden, 2001. "Estimating features of a distribution from binomial data," CeMMAP working papers CWP07/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    2. Arthur Lewbel & Xun Tang, 2012. "Identification and Estimation of Games with Incomplete Information Using Excluded Regressors," Boston College Working Papers in Economics 808, Boston College Department of Economics, revised 05 Mar 2013.
    3. Thierry Kalisa & Mary Riddel & W. Douglass Shaw, 2016. "Willingness to pay to avoid arsenic-related risks: a special regressor approach," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 5(2), pages 143-162, July.
    4. Magnac, Thierry & Maurin, Eric, 2007. "Identification and information in monotone binary models," Journal of Econometrics, Elsevier, vol. 139(1), pages 76-104, July.
    5. Arthur Lewbel & Oliver Linton, 2003. "Nonparametric estimation of homothetic and homothetically separable functions," CeMMAP working papers CWP14/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Williams, Benjamin, 2020. "Nonparametric identification of discrete choice models with lagged dependent variables," Journal of Econometrics, Elsevier, vol. 215(1), pages 286-304.
    7. Fosgerau, Mogens, 2006. "Investigating the distribution of the value of travel time savings," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 688-707, September.
    8. Yingying Dong & Arthur Lewbel & Thomas Tao Yang, 2012. "Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors," Boston College Working Papers in Economics 789, Boston College Department of Economics, revised 15 May 2012.
    9. Bo E. Honore & Arthur Lewbel, 1998. "Semiparametric Binary Choice Panel Data Models without Strictly Exogeneous Regressors," Boston College Working Papers in Economics 455, Boston College Department of Economics, revised 22 Sep 2001.
    10. Jones, Benjamin A. & Ripberger, Joseph & Jenkins-Smith, Hank & Silva, Carol, 2017. "Estimating willingness to pay for greenhouse gas emission reductions provided by hydropower using the contingent valuation method," Energy Policy, Elsevier, vol. 111(C), pages 362-370.
    11. Arthur Lewbel, 2006. "Modeling Heterogeneity," Boston College Working Papers in Economics 650, Boston College Department of Economics.
    12. Yingying Dong & Arthur Lewbel, 2012. "A Simple Estimator for Binary Choice Models With Endogenous Regressors," Boston College Working Papers in Economics 807, Boston College Department of Economics.
    13. Arthur Lewbel, 2012. "An Overview of the Special Regressor Method," Boston College Working Papers in Economics 810, Boston College Department of Economics.
    14. Mogens Fosgerau, 2014. "Nonparametric approaches to describing heterogeneity," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 11, pages 257-267, Edward Elgar Publishing.
    15. Arthur Lewbel, 2002. "Ordered Response Threshold Estimation," Boston College Working Papers in Economics 535, Boston College Department of Economics, revised 29 Oct 2003.
    16. Matzkin, Rosa L., 2012. "Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 166(1), pages 106-115.
    17. Arthur Lewbel & Yingying Dong & Thomas Tao Yang, 2012. "Viewpoint: Comparing features of convenient estimators for binary choice models with endogenous regressors," Canadian Journal of Economics, Canadian Economics Association, vol. 45(3), pages 809-829, August.
    18. Lewbel, Arthur, 2007. "Endogenous selection or treatment model estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 777-806, December.
    19. Kim, Sooil, 2006. "Bias and Efficiency of Uniform Bid Design in Contingent Valuation," 2006 Annual meeting, July 23-26, Long Beach, CA 21335, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Mogens Fosgerau, 2005. "Specification Of A Model To Measure The Value Of Travel Time Savings From Binomial Data," Urban/Regional 0508008, University Library of Munich, Germany.
    21. Robert J. Johnston & Kevin J. Boyle & Wiktor (Vic) Adamowicz & Jeff Bennett & Roy Brouwer & Trudy Ann Cameron & W. Michael Hanemann & Nick Hanley & Mandy Ryan & Riccardo Scarpa & Roger Tourangeau & Ch, 2017. "Contemporary Guidance for Stated Preference Studies," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(2), pages 319-405.
    22. Chu, Ba & Jacho-Chávez, David T., 2012. "k-NEAREST NEIGHBOR ESTIMATION OF INVERSE-DENSITY-WEIGHTED EXPECTATIONS WITH DEPENDENT DATA," Econometric Theory, Cambridge University Press, vol. 28(4), pages 769-803, August.
    23. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    24. Fosgerau, Mogens & Hjort, Katrine & Vincent Lyk-Jensen, Stéphanie, 2007. "An approach to the estimation of the distribution of marginal valuations from discrete choice data," MPRA Paper 3907, University Library of Munich, Germany.
    25. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
    26. Arthur Lewbel, 2005. "Simple Endogenous Binary Choice and Selection Panel Model Estimators," Boston College Working Papers in Economics 613, Boston College Department of Economics, revised 04 Sep 2006.
    27. Daniel McFadden, 2008. "Semiparametric analysis (in Russian)," Quantile, Quantile, issue 5, pages 29-40, September.
    28. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.
    29. Arthur Lewbel & Susanne M. Schennach, 2003. "A Simple Ordered Data Estimator For Inverse Density Weighted Functions," Boston College Working Papers in Economics 557, Boston College Department of Economics, revised 01 May 2005.
    30. Arthur Lewbel & Oliver Linton, 2003. "Nonparametric Matching and Efficient Estimators of Homothetically Separable Functions," Boston College Working Papers in Economics 585, Boston College Department of Economics, revised 04 Sep 2006.
    31. Arthur Lewbel & Xun Tang, 2010. "Identification and Estimation of Games with Incomplete Information Using Excluded Regressors, Second Version," PIER Working Paper Archive 12-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 20 Mar 2012.
    32. Jones, Benjamin A. & Berrens, Robert P. & Jenkins-Smith, Hank & Silva, Carol & Ripberger, Joe & Carlson, Deven & Gupta, Kuhika & Wehde, Wesley, 2018. "In search of an inclusive approach: Measuring non-market values for the effects of complex dam, hydroelectric and river system operations," Energy Economics, Elsevier, vol. 69(C), pages 225-236.

  107. Linton, Oliver B. & Perch Nielsen, Jens & Van de Geer, Sara, 2001. "Estimating Multiplicative and Additive Hazard Functions by Kernel Methods," Finance Working Papers 01-2, University of Aarhus, Aarhus School of Business, Department of Business Studies.

    Cited by:

    1. Toshio Honda, 2005. "Estimation in additive cox models by marginal integration," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 403-423, September.

  108. Linton, Oliver & Xiao, Zhijie, 2001. "Second-order approximation for adaptive regression estimators," LSE Research Online Documents on Economics 317, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Hidehiko Ichimura & Oliver Linton, 2003. "Asymptotic Expansions for Some Semiparametric Program Evaluation Estimators," STICERD - Econometrics Paper Series 451, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Cattaneo, Matias D. & Crump, Richard K. & Jansson, Michael, 2012. "Optimal inference for instrumental variables regression with non-Gaussian errors," Journal of Econometrics, Elsevier, vol. 167(1), pages 1-15.
    3. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    4. Hafner, Christian & Linton, Oliver & Wang, Linqi, 2022. "Dynamic Autoregressive Liquidity (DArLiQ)," LIDAM Discussion Papers LFIN 2022002, Université catholique de Louvain, Louvain Finance (LFIN).
    5. Tamaki, Kenichiro, 2007. "Second order optimality for estimators in time series regression models," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 638-659, March.

  109. Hidehiko Ichimura & Oliver Linton, 2001. "Asymptotic expansions for some semiparametric program evaluation estimators," CeMMAP working papers CWP04/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Toru Kitagawa & Chris Muris, 2015. "Model averaging in semiparametric estimation of treatment effects," CeMMAP working papers CWP46/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Annette Bergemann & Bernd Fitzenberger & Stefan Speckesser, 2009. "Evaluating the dynamic employment effects of training programs in East Germany using conditional difference-in-differences," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 797-823.
    4. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    5. Fitzenberger, Bernd & Speckesser, Stefan, 2005. "Employment Effects of the Provision of Specific Professional Skills and Techniques in Germany," ZEW Discussion Papers 05-77, ZEW - Leibniz Centre for European Economic Research.
    6. Bernd Fitzenberger & Aderonke Osikominu & Robert Völter, 2008. "Get Training or Wait? Long-Run Employment Effects of Training Programs for the Unemployed in West Germany," Annals of Economics and Statistics, GENES, issue 91-92, pages 321-355.
    7. Michael Lechner, 2004. "Sequential Matching Estimation of Dynamic Causal Models," University of St. Gallen Department of Economics working paper series 2004 2004-06, Department of Economics, University of St. Gallen.
    8. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    9. Toru Kitagawa & Chris Muris, 2013. "Covariate selection and model averaging in semiparametric estimation of treatment effects," CeMMAP working papers 61/13, Institute for Fiscal Studies.
    10. Galdo, Jose C. & Smith, Jeffrey A. & Black, Dan A., 2007. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," IZA Discussion Papers 3095, Institute of Labor Economics (IZA).
    11. Biewen, Martin & Fitzenberger, Bernd & Osikominu, Aderonke & Waller, Marie, 2007. "Which Program for Whom? Evidence on the Comparative Effectiveness of Public Sponsored Training Programs in Germany," ZEW Discussion Papers 07-042, ZEW - Leibniz Centre for European Economic Research.
    12. Millimet, Daniel L. & Tchernis, Rusty, 2009. "On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 397-415.

  110. Chen, Xiaohong & Linton, Oliver & Robinson, Peter, 2001. "The estimation of conditional densities," LSE Research Online Documents on Economics 2312, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Jan G. de Gooijer & Dawit Zerom, 2002. "On Conditional Density Estimation," Tinbergen Institute Discussion Papers 02-032/4, Tinbergen Institute.
    2. Kotlyarova, Yulia & Schafgans, Marcia M.A. & Zinde-Walsh, Victoria, 2021. "Rates of expansions for functional estimators," LSE Research Online Documents on Economics 113436, London School of Economics and Political Science, LSE Library.
    3. Buch-Kromann, Tine & Guillén, Montserrat & Linton, Oliver & Nielsen, Jens Perch, 2011. "Multivariate density estimation using dimension reducing information and tail flattening transformations," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 99-110, January.
    4. Kateřina Konečná & Ivanka Horová, 2019. "Maximum likelihood method for bandwidth selection in kernel conditional density estimate," Computational Statistics, Springer, vol. 34(4), pages 1871-1887, December.
    5. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    6. Tindara Addabbo & Donata Favaro & Stefano Magrini, 2012. "Gender differences in productivity rewards: the role of human capital," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 59(1), pages 81-110, March.
    7. Donata Favaro & Stefano Magrini, 2005. "Group versus individual discrimination among young workers: a distributional approach," Labor and Demography 0506003, University Library of Munich, Germany.
    8. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2012. "International market links and volatility transmission," Journal of Econometrics, Elsevier, vol. 170(1), pages 117-141.
    9. Tindara Addabbo & Donata Favaro & Stefano Magrini, 2010. "Gender differences in productivity rewards in Italy: the role of human capital," Working Papers 2010_11, Department of Economics, University of Venice "Ca' Foscari".
    10. Alejandro Quintela-Del-Río, 2008. "Hazard function given a functional variable: Non-parametric estimation under strong mixing conditions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(5), pages 413-430.
    11. Amaro de Matos, Joao & Fernandes, Marcelo, 2007. "Testing the Markov property with high frequency data," Journal of Econometrics, Elsevier, vol. 141(1), pages 44-64, November.
    12. Otneim, Håkon & Tjøstheim, Dag, 2016. "Non-parametric estimation of conditional densities: A new method," Discussion Papers 2016/22, Norwegian School of Economics, Department of Business and Management Science.

  111. Oliver Linton & Douglas J.Hodgson & Keith Vorkink, 2001. "Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach," FMG Discussion Papers dp382, Financial Markets Group.

    Cited by:

    1. Sentana, Enrique & Fiorentini, Gabriele, 2018. "Specification tests for non-Gaussian maximum likelihood estimators," CEPR Discussion Papers 12934, C.E.P.R. Discussion Papers.
    2. Hodgson, Douglas J. & Linton, Oliver & Vorkink, Keith, 2004. "Testing forward exchange rate unbiasedness efficiently: A semiparametric approach," Journal of Applied Economics, Universidad del CEMA, vol. 7(2), pages 1-29, November.
    3. Sima M. Fortsch & Jeong Hoon Choi & Elena A. Khapalova, 2022. "Competition can help predict sales," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 331-344, March.
    4. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
    5. Marie-Claude Beaulieu & Jean-Marie Dufour & Lynda Khalaf, 2005. "Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions," Springer Books, in: Michèle Breton & Hatem Ben-Ameur (ed.), Numerical Methods in Finance, chapter 0, pages 173-191, Springer.
    6. Hallin, M. & Werker, B.J.M. & van den Akker, R., 2015. "Optimal Pseudo-Gaussian and Rank-based Tests of the Cointegration Rank in Semiparametric Error-correction Models," Discussion Paper 2015-001, Tilburg University, Center for Economic Research.
    7. Sakhanenko, Lyudmila, 2008. "Testing for ellipsoidal symmetry: A comparison study," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 565-581, December.
    8. Danilo Leal & Rodrigo Jiménez & Marco Riquelme & Víctor Leiva, 2023. "Elliptical Capital Asset Pricing Models: Formulation, Diagnostics, Case Study with Chilean Data, and Economic Rationale," Mathematics, MDPI, vol. 11(6), pages 1-27, March.
    9. Hodgson, Douglas J & Vorkink, Keith P, 2003. "Efficient Estimation of Conditional Asset-Pricing Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 269-283, April.
    10. Amengual, Dante & Sentana, Enrique, 2010. "A comparison of mean-variance efficiency tests," Journal of Econometrics, Elsevier, vol. 154(1), pages 16-34, January.
    11. Taras Bodnar & Yarema Okhrin & Valdemar Vitlinskyy & Taras Zabolotskyy, 2018. "Determination and estimation of risk aversion coefficients," Computational Management Science, Springer, vol. 15(2), pages 297-317, June.
    12. Rombouts, Jeroen V. K. & Hafner, Christian M., 2004. "Semiparametric multivariate volatility models," Papers 2004,14, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    13. Bodnar, Taras & Reiß, Markus, 2016. "Exact and asymptotic tests on a factor model in low and large dimensions with applications," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 125-151.
    14. Gabriele Fiorentini & Enrique Sentana, 2018. "Consistent non-Gaussian pseudo maximum likelihood estimators," Working Paper series 18-06, Rimini Centre for Economic Analysis.
    15. Sladana Babic & Laetitia Gelbgras & Marc Hallin & Christophe Ley, 2019. "Optimal tests for elliptical symmetry: specified and unspecified location," Working Papers ECARES 2019-26, ULB -- Universite Libre de Bruxelles.
    16. Gabriele Fiorentini & Enrique Sentana, 2007. "On the efficiency and consistency of likelihood estimation in multivariate conditionally heteroskedastic dynamic regression models," Working Paper series 38_07, Rimini Centre for Economic Analysis.
    17. Beaulieu, Marie-Claude & Dufour, Jean-Marie & Khalaf, Lynda, 2010. "Asset-pricing anomalies and spanning: Multivariate and multifactor tests with heavy-tailed distributions," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 763-782, September.
    18. Hailong Qian & Heather L. Bednarek, 2015. "Partial efficient estimation of SUR models," Economics Bulletin, AccessEcon, vol. 35(1), pages 338-348.
    19. Taras Bodnar & Arjun K. Gupta & Valdemar Vitlinskyi & Taras Zabolotskyy, 2019. "Statistical Inference for the Beta Coefficient," Risks, MDPI, vol. 7(2), pages 1-14, May.
    20. Vitali Alexeev & Alex Maynard, 2010. "Localized Level Crossing Random Walk Test Robust to the Presence of Structural Breaks," Working Papers 1001, University of Guelph, Department of Economics and Finance.
    21. Enrique Sentana, 2008. "The Econometrics of Mean-Variance Efficiency Tests: A Survey," Working Papers wp2008_0807, CEMFI.
    22. Douglas Hodgson & Barrett Slade & Keith Vorkink, 2006. "Constructing Commercial Indices: A Semiparametric Adaptive Estimator Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 32(2), pages 151-168, March.
    23. Peremans, Kris & Van Aelst, Stefan, 2018. "Robust inference for seemingly unrelated regression models," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 212-224.
    24. Asimit, Alexandru V. & Jones, Bruce L., 2007. "Extreme behavior of bivariate elliptical distributions," Insurance: Mathematics and Economics, Elsevier, vol. 41(1), pages 53-61, July.
    25. Dawood Ashraf, 2016. "Does Shari’ah Screening Cause Abnormal Returns? Empirical Evidence from Islamic Equity Indices," Journal of Business Ethics, Springer, vol. 134(2), pages 209-228, March.
    26. Francq, C. & Jiménez-Gamero, M.D. & Meintanis, S.G., 2017. "Tests for conditional ellipticity in multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 196(2), pages 305-319.
    27. Vintilă Georgeta & Păunescu Radu Alin, 2015. "Econometric Tests of the CAPM Model for a Portfolio Composed of Companies Listed on Nasdaq and Dow Jones Components," Scientific Annals of Economics and Business, Sciendo, vol. 62(3), pages 453-480, November.
    28. Hallin, Marc & van den Akker, Ramon & Werker, Bas J.M., 2016. "Semiparametric error-correction models for cointegration with trends: Pseudo-Gaussian and optimal rank-based tests of the cointegration rank," Journal of Econometrics, Elsevier, vol. 190(1), pages 46-61.
    29. Manuel Galea & David Cademartori & Roberto Curci & Alonso Molina, 2020. "Robust Inference in the Capital Asset Pricing Model Using the Multivariate t -distribution," JRFM, MDPI, vol. 13(6), pages 1-22, June.
    30. Zheyuan Zhang & Huiying Wu & Sammy Xiaoyan Ying & Jiaxing You, 2023. "Corporate Innovation and Disclosure Strategy," Abacus, Accounting Foundation, University of Sydney, vol. 59(1), pages 76-133, March.
    31. Guermat, Cherif & Freeman, Mark C., 2010. "A net beta test of asset pricing models," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 1-9, January.

  112. Mototsugu Shintani & Oliver Linton, 2001. "Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors," Vanderbilt University Department of Economics Working Papers 0111, Vanderbilt University Department of Economics.

    Cited by:

    1. Musselwhite, Gary & Herath, Gamini, 2007. "Chaos theory and assessment of forest stakeholder attitudes towards Australian forest policy," Forest Policy and Economics, Elsevier, vol. 9(8), pages 947-964, May.
    2. Tapas MISHRA & Mamata PARHI & Claude DIEBOLT, 2014. "Evolutionary efficiency and distributive effects of inertia in cross-country life-satisfaction," Economies et Sociétés (Serie 'Histoire Economique Quantitative'), Association Française de Cliométrie (AFC), issue 49, pages 1335-1356, Août.
    3. Park, Joon Y. & Whang, Yoon-Jae, 2012. "Random walk or chaos: A formal test on the Lyapunov exponent," Journal of Econometrics, Elsevier, vol. 169(1), pages 61-74.
    4. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    5. Cars Hommes & Sebastiano Manzan, 2006. "Testing for Nonlinear Structure and Chaos in Economic Time. A Comment," Tinbergen Institute Discussion Papers 06-030/1, Tinbergen Institute.
    6. Kyrtsou, Catherine & Serletis, Apostolos, 2006. "Univariate tests for nonlinear structure," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 154-168, March.
    7. Serletis, Apostolos & He, Mingyu & Chowdhury, M.M. Islam, 2023. "Chaos in long-maturity real rates," Economics Letters, Elsevier, vol. 225(C).
    8. Shintani, Mototsugu, 2008. "A dynamic factor approach to nonlinear stability analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 32(9), pages 2788-2808, September.
    9. Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero & Julián Andrada-Félix, "undated". "Testing Chaotic Dynamics via Lyapunov Exponents," Working Papers 2000-07, FEDEA.
    10. Sandubete, Julio E. & Escot, Lorenzo, 2020. "Chaotic signals inside some tick-by-tick financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    11. Elena Olmedo & Ricardo Gimeno & Lorenzo Escot & Ruth Mateos, 2007. "Convergencia y Estabilidad de los Tipos de Cambio Europeos: Una Aplicación de Exponentes de Lyapunov," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 44(129), pages 91-108.
    12. Serletis, Apostolos & Shintani, Mototsugu, 2006. "Chaotic monetary dynamics with confidence," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 228-252, March.
    13. Shintani, Mototsugu & Linton, Oliver, 2002. "Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos," LSE Research Online Documents on Economics 2093, London School of Economics and Political Science, LSE Library.
    14. Orzeszko, Witold, 2008. "The new method of measuring the effects of noise reduction in chaotic data," Chaos, Solitons & Fractals, Elsevier, vol. 38(5), pages 1355-1368.
    15. Mototsugu Shintani, 2002. "A Nonparametric Measure of Convergence Toward Purchasing Power Parity," Vanderbilt University Department of Economics Working Papers 0219, Vanderbilt University Department of Economics, revised Jul 2004.
    16. Vitaliy Vandrovych, 2005. "Study of Nonlinearities in the Dynamics of Exchange Rates: Is There Any Evidence of Chaos?," Computing in Economics and Finance 2005 234, Society for Computational Economics.
    17. Serletis, Apostolos & Shahmoradi, Asghar, 2007. "Chaos, self-organized criticality, and SETAR nonlinearity: An analysis of purchasing power parity between Canada and the United States," Chaos, Solitons & Fractals, Elsevier, vol. 33(5), pages 1437-1444.
    18. Hommes, C.H. & Manzan, S., 2005. "Testing for Nonlinear Structure and Chaos in Economic Time Series: A Comment," CeNDEF Working Papers 05-14, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    19. Escot, Lorenzo & Sandubete, Julio E., 2023. "Estimating Lyapunov exponents on a noisy environment by global and local Jacobian indirect algorithms," Applied Mathematics and Computation, Elsevier, vol. 436(C).
    20. Orlando Gomes, 2004. "Heterogeneous Researchers in a Two-Sector Representative Consumer Economy," GE, Growth, Math methods 0409009, University Library of Munich, Germany.
    21. Vogl, Markus, 2022. "Controversy in financial chaos research and nonlinear dynamics: A short literature review," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    22. Mototsugu Shintani, 2006. "A nonparametric measure of convergence towards purchasing power parity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 589-604, July.
    23. Serletis, Apostolos & Uritskaya, Olga Y., 2007. "Detecting signatures of stochastic self-organization in US money and velocity measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 281-291.
    24. Serletis, Apostolos & Shahmoradi, Asghar & Serletis, Demitre, 2007. "Effect of noise on estimation of Lyapunov exponents from a time series," Chaos, Solitons & Fractals, Elsevier, vol. 32(2), pages 883-887.
    25. Pogany, Peter, 2010. "What’s wrong with the world? Rationality! A critique of economic anthropology in the spirit of Jean Gebser," MPRA Paper 26458, University Library of Munich, Germany.
    26. Tapas Mishra & Mamata Parhi & Raúl Fuentes, 2015. "How Interdependent are Cross-Country Happiness Dynamics?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 122(2), pages 491-518, June.

  113. Linton, Oliver, 2000. "Efficient estimation of generalized additive nonparametric regression models," LSE Research Online Documents on Economics 314, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Gayle, Wayne-Roy, 2013. "Identification and N-consistent estimation of a nonlinear panel data model with correlated unobserved effects," Journal of Econometrics, Elsevier, vol. 175(2), pages 71-83.
    2. Oliver B. Linton & Enno Mammen & J. Nielsen & Carsten Tanggaard, 2000. "Yield Curve Estimation by Kernel Smoothing Methods," Econometric Society World Congress 2000 Contributed Papers 0235, Econometric Society.
    3. Cai, Zongwu & Li, Qi & Park, Joon Y., 2009. "Functional-coefficient models for nonstationary time series data," Journal of Econometrics, Elsevier, vol. 148(2), pages 101-113, February.
    4. Kalnina, Ilze & Linton, Oliver, 2007. "Inference about realized volatility using infill subsampling," LSE Research Online Documents on Economics 4411, London School of Economics and Political Science, LSE Library.
    5. Arthur Lewbel & Krishna Pendakur, 2017. "Unobserved Preference Heterogeneity in Demand Using Generalized Random Coefficients," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1100-1148.
    6. Enno Mammen & Oliver Linton, 2004. "Estimating Semiparametric ARCH Models by Kernel Smoothing Methods," FMG Discussion Papers dp511, Financial Markets Group.
    7. David Jacho-Chavez & Arthur Lewbel & Oliver Linton, 2006. "Identification and Nonparametric Estimation of a Transformed Additively Separable Model," Boston College Working Papers in Economics 652, Boston College Department of Economics, revised 26 Nov 2008.
    8. Manzan, sebastiano & Zerom, Dawit, 2008. "A Semiparametric Analysis of Gasoline Demand in the US: Reexamining The Impact of Price," MPRA Paper 14386, University Library of Munich, Germany.
    9. Dong, C. & Li, S., 2021. "Specification Lasso and an Application in Financial Markets," Cambridge Working Papers in Economics 2139, Faculty of Economics, University of Cambridge.
    10. Azomahou, Theophile & Diene, Bity & Diene, Mbaye, 2009. "Technology frontier, labor productivity and economic growth: Evidence from OECD countries," MERIT Working Papers 2009-059, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    11. Koo, Bonsoo & Linton, Oliver, 2012. "Estimation of semiparametric locally stationary diffusion models," Journal of Econometrics, Elsevier, vol. 170(1), pages 210-233.
    12. Ben-Moshe, Dan & D’Haultfœuille, Xavier & Lewbel, Arthur, 2017. "Identification of additive and polynomial models of mismeasured regressors without instruments," Journal of Econometrics, Elsevier, vol. 200(2), pages 207-222.
    13. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Toshio Honda, 2005. "Estimation in additive cox models by marginal integration," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 403-423, September.
    15. Ozabaci, Deniz & Henderson, Daniel J., 2014. "Additive Kernel Estimates of Returns to Schooling," IZA Discussion Papers 8736, Institute of Labor Economics (IZA).
    16. Li, Qi & Hsiao, Cheng & Zinn, Joel, 2003. "Consistent specification tests for semiparametric/nonparametric models based on series estimation methods," Journal of Econometrics, Elsevier, vol. 112(2), pages 295-325, February.
    17. Tu, Yundong & Wang, Ying, 2022. "Spurious functional-coefficient regression models and robust inference with marginal integration," Journal of Econometrics, Elsevier, vol. 229(2), pages 396-421.
    18. Gozalo, Pedro L. & Linton, Oliver B., 2001. "Testing additivity in generalized nonparametric regression models with estimated parameters," Journal of Econometrics, Elsevier, vol. 104(1), pages 1-48, August.
    19. Azomahou, Theophile & Diene, Bity & Diene, Mbaye, 2012. "Nonlinearities in productivity growth: A semi-parametric panel analysis," MERIT Working Papers 2012-046, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    20. Kim, Woocheol & Linton, Oliver, 2004. "A local instrumental variable estimation method for generalized additive volatility models," LSE Research Online Documents on Economics 24758, London School of Economics and Political Science, LSE Library.
    21. Barry K. Goodwin & Matthew T. Holt & Jeffrey P. Prestemon, 2021. "Semi-parametric models of spatial market integration," Empirical Economics, Springer, vol. 61(5), pages 2335-2361, November.
    22. Oliver Linton & E. Mammen & J. Nielsen & C. Tanggaard, 1998. "Estimating Yield Curves by Kernel Smoothing Methods," Cowles Foundation Discussion Papers 1205, Cowles Foundation for Research in Economics, Yale University.
    23. Linton, Oliver & Mammen, Enno, 2004. "Estimating semiparametric ARCH (∞) models by kernel smoothing methods," LSE Research Online Documents on Economics 24762, London School of Economics and Political Science, LSE Library.
    24. O. Linton & E. Mammen, 2005. "Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods," Econometrica, Econometric Society, vol. 73(3), pages 771-836, May.
    25. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    26. Moral, Ignacio & Rodriguez-Poo, Juan M., 2004. "An efficient marginal integration estimator of a semiparametric additive modelling," Statistics & Probability Letters, Elsevier, vol. 69(4), pages 451-463, October.
    27. Gao, Jiti & King, Maxwell, 2003. "Estimation and model specification testing in nonparametric and semiparametric econometric models," MPRA Paper 11989, University Library of Munich, Germany, revised Feb 2006.
    28. Théophile T. Azomahou & Bity Diene & Mbaye Diene, 2009. "Technology frontier, labor productivity and economic growth: Evidence from OECD countries," DEM Discussion Paper Series 09-19, Department of Economics at the University of Luxembourg.
    29. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    30. Azomahou, Théophile T. & Diene, Bity & Diene, Mbaye, 2013. "Nonlinearities in productivity growth: A semi-parametric panel analysis," Structural Change and Economic Dynamics, Elsevier, vol. 24(C), pages 45-75.
    31. Lin, Huazhen & Pan, Lixian & Lv, Shaogao & Zhang, Wenyang, 2018. "Efficient estimation and computation for the generalised additive models with unknown link function," Journal of Econometrics, Elsevier, vol. 202(2), pages 230-244.
    32. Barry K. Goodwin, 2014. "Comment on "The Evolving Relationships between Agricultural and Energy Commodity Prices: A Shifting-Mean Vector Autoregressive Analysis"," NBER Chapters, in: The Economics of Food Price Volatility, pages 187-192, National Bureau of Economic Research, Inc.
    33. Su, Liangjun & Ullah, Aman, 2008. "Local polynomial estimation of nonparametric simultaneous equations models," Journal of Econometrics, Elsevier, vol. 144(1), pages 193-218, May.
    34. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.

  114. Arthur Lewbel & Oliver Linton, 2000. "Nonparametric Censored and Truncated Regression," Boston College Working Papers in Economics 439, Boston College Department of Economics.

    Cited by:

    1. Chen, Xiaohong & Linton, Oliver & Van Keilegom, Ingrid, 2003. "Estimation of semiparametric models when the criterion function is not smooth," LSE Research Online Documents on Economics 2167, London School of Economics and Political Science, LSE Library.
    2. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    3. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    4. Lu, Xuewen & Burke, M.D., 2005. "Censored multiple regression by the method of average derivatives," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 182-205, July.
    5. Taylor, Luke & Otsu, Taisuke, 2016. "Estimation of nonseparable models with censored dependent variables and endogenous regressors," LSE Research Online Documents on Economics 68678, London School of Economics and Political Science, LSE Library.
    6. Dubois, Pierre & de Mouzon, Olivier & Scott Morton, Fiona & Seabright, Paul, 2011. "Market Size and Pharmaceutical Innovation," IDEI Working Papers 670, Institut d'Économie Industrielle (IDEI), Toulouse, revised Mar 2014.
    7. Chrysovalantis Gaganis & Fotios Pasiouras & Menelaos Tasiou & Constantin Zopounidis, 2021. "CISEF: A composite index of social, environmental and financial performance," Post-Print hal-03113006, HAL.
    8. Joseph G. Altonji & Hidehiko Ichimura & Taisuke Otsu, 2008. "Estimating Derivatives in Nonseparable Models with Limited Dependent Variables," Cowles Foundation Discussion Papers 1668R, Cowles Foundation for Research in Economics, Yale University, revised May 2011.
    9. Stéphane Girard & Gilles Claude Stupfler & Antoine Usseglio-Carleve, 2021. "Extreme Conditional Expectile Estimation in Heavy-Tailed Heteroscedastic Regression Models," Post-Print hal-03306230, HAL.
    10. Susanne M. Schennach, 2015. "A bias bound approach to nonparametric inference," CeMMAP working papers CWP71/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Cizek, P., 2008. "Semiparametric Robust Estimation of Truncated and Censored Regression Models," Other publications TiSEM a6228ada-1ab5-47ee-9d23-4, Tilburg University, School of Economics and Management.
    12. Sujica, Aleksandar & Van Keilegom, Ingrid, 2018. "The copula-graphic estimator in censored nonparametric location-scale regression models," Econometrics and Statistics, Elsevier, vol. 7(C), pages 89-114.
    13. Anastasia Semykina & Jeffrey M. Wooldridge, 2018. "Binary response panel data models with sample selection and self‐selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 179-197, March.
    14. Byeong U. Park & Leopold Simar & Valentin Zelenyuk, 2008. "Local Likelihood Estimation of Truncated Regression and Its Partial Derivatives: Theory and Application," Discussion Papers 7, Kyiv School of Economics.
    15. Anil Kumar, 2012. "Nonparametric estimation of the impact of taxes on female labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 415-439, April.
    16. Dahl, Christian M. & Effraimidis, Georgios & Pedersen, Mikkel H., 2019. "Nonparametric wind power forecasting under fixed and random censoring," Energy Economics, Elsevier, vol. 84(C).
    17. Chen, Songnian & Khan, Shakeeb, 2003. "Rates of convergence for estimating regression coefficients in heteroskedastic discrete response models," Journal of Econometrics, Elsevier, vol. 117(2), pages 245-278, December.
    18. Lu, Xuewen, 2010. "Asymptotic distributions of two "synthetic data" estimators for censored single-index models," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 999-1015, April.
    19. Chesher, Andrew, 2009. "Excess heterogeneity, endogeneity and index restrictions," Journal of Econometrics, Elsevier, vol. 152(1), pages 37-45, September.
    20. Karlsson, Maria & Cantoni, Eva & de Luna, Xavier, 2009. "Local polynomial regression with truncated or censored response," Working Paper Series 2009:25, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    21. Ai, Chunrong & Li, Hongjun & Lin, Zhongjian & Meng, Meixia, 2015. "Estimation of panel data partly specified Tobit regression with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 316-326.
    22. Lu, Xuewen & Cheng, Tsung-Lin, 2007. "Randomly censored partially linear single-index models," Journal of Multivariate Analysis, Elsevier, vol. 98(10), pages 1895-1922, November.
    23. Matzkin, Rosa L., 2012. "Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 166(1), pages 106-115.
    24. Jinzhi Bu & David Simchi-Levi & Li Wang, 2023. "Offline Pricing and Demand Learning with Censored Data," Management Science, INFORMS, vol. 69(2), pages 885-903, February.
    25. Marcela Munizaga & Sergio Jara-Díaz & Paulina Greeven & Chandra Bhat, 2008. "Econometric Calibration of the Joint Time Assignment--Mode Choice Model," Transportation Science, INFORMS, vol. 42(2), pages 208-219, May.
    26. Maria Karlsson & Thomas Laitila, 2014. "Finite mixture modeling of censored regression models," Statistical Papers, Springer, vol. 55(3), pages 627-642, August.
    27. Oberhofer, Walter & Haupt, Harry, 2005. "Consistency of nonlinear regression quantiles under Type I censoring weak dependence and general covariate design," University of Regensburg Working Papers in Business, Economics and Management Information Systems 406, University of Regensburg, Department of Economics.
    28. Cao, Ricardo & Gonzalez-Manteiga, Wenceslao, 2008. "Goodness-of-fit tests for conditional models under censoring and truncation," Journal of Econometrics, Elsevier, vol. 143(1), pages 166-190, March.
    29. Jing Cheng & Dylan S. Small, 2021. "Semiparametric models and inference for the effect of a treatment when the outcome is nonnegative with clumping at zero," Biometrics, The International Biometric Society, vol. 77(4), pages 1187-1201, December.
    30. Elias Ould-Saïd & Mohamed Lemdani, 2006. "Asymptotic Properties of a Nonparametric Regression Function Estimator with Randomly Truncated Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(2), pages 357-378, June.
    31. Rodriguez Poo, Juan M. & Sperlich, Stefan & Vieu, Philippe, 2000. "Semiparametric estimation of weak and strong separable models," DES - Working Papers. Statistics and Econometrics. WS 10064, Universidad Carlos III de Madrid. Departamento de Estadística.
    32. Feve, Frederique & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2012. "Estimation of conditional ranks and tests of exogeneity in nonparametric nonseparable models," LIDAM Discussion Papers ISBA 2012036, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    33. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    34. Chen, Songnian & Zhou, Yahong, 2010. "Semiparametric and nonparametric estimation of sample selection models under symmetry," Journal of Econometrics, Elsevier, vol. 157(1), pages 143-150, July.
    35. Efang Kong & Oliver Linton & Yingcun Xia, 2011. "Global Bahadur representation for nonparametric censored regression quantiles and its applications," CeMMAP working papers CWP33/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    36. Koul, Hira L. & Song, Weixing & Liu, Shan, 2014. "Model checking in Tobit regression via nonparametric smoothing," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 36-49.
    37. Moral-Arce, Ignacio & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 2011. "Low dimensional semiparametric estimation in a censored regression model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 118-129, January.

  115. Linton, Oliver, 2000. "Edgeworth approximations for semiparametric instrumental variable estimators and test statistics," LSE Research Online Documents on Economics 2156, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Marine Carrasco & Rachidi Kotchoni, 2013. "Efficient estimation using the Characteristic Function," CIRANO Working Papers 2013s-22, CIRANO.
    2. Kundhi, Gubhinder & Rilstone, Paul, 2012. "Edgeworth expansions for GEL estimators," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 118-146.
    3. Carrasco, Marine, 2012. "A regularization approach to the many instruments problem," Journal of Econometrics, Elsevier, vol. 170(2), pages 383-398.
    4. Chen, Songnian & Zhang, Hanghui, 2015. "Binary quantile regression with local polynomial smoothing," Journal of Econometrics, Elsevier, vol. 189(1), pages 24-40.
    5. Matsushita, Yukitoshi & Otsu, Taisuke, 2020. "Likelihood inference on semiparametric models with generated regressors," LSE Research Online Documents on Economics 102696, London School of Economics and Political Science, LSE Library.
    6. Paul Rilstone, 2021. "Higher-Order Stochastic Expansions and Approximate Moments for Non-linear Models with Heterogeneous Observations," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 99-120, December.
    7. Juan Carlos Escanciano, 2010. "The Integrated Instrumental Variables Estimator: Exploiting Nonlinearities for Identification of Linear Models," CAEPR Working Papers 2010-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

  116. Oliver B. Linton & Enno Mammen & J. Nielsen & Carsten Tanggaard, 2000. "Yield Curve Estimation by Kernel Smoothing Methods," Econometric Society World Congress 2000 Contributed Papers 0235, Econometric Society.

    Cited by:

    1. David Bolder & Scott Gusba, 2002. "Exponentials, Polynomials, and Fourier Series: More Yield Curve Modelling at the Bank of Canada," Staff Working Papers 02-29, Bank of Canada.
    2. Enno Mammen & Oliver Linton, 2004. "Estimating Semiparametric ARCH Models by Kernel Smoothing Methods," FMG Discussion Papers dp511, Financial Markets Group.
    3. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Laurini, Márcio P. & Hotta, Luiz K., 2008. "Bayesian extensions to diebold-li term structure model," Insper Working Papers wpe_122, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    5. Yan Liu & Jing Cynthia Wu, 2020. "Reconstructing the Yield Curve," NBER Working Papers 27266, National Bureau of Economic Research, Inc.
    6. Andreasen, Martin M. & Christensen, Bent Jesper, 2015. "The SR approach: A new estimation procedure for non-linear and non-Gaussian dynamic term structure models," Journal of Econometrics, Elsevier, vol. 184(2), pages 420-451.
    7. Tong, Xiaojun & He, Zhuoqiong Chong & Sun, Dongchu, 2018. "Estimating Chinese Treasury yield curves with Bayesian smoothing splines," Econometrics and Statistics, Elsevier, vol. 8(C), pages 94-124.
    8. Wali Ullah & Yasumasa Matsuda & Yoshihiko Tsukuda, 2014. "Dynamics of the term structure of interest rates and monetary policy: is monetary policy effective during zero interest rate policy?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 546-572, March.
    9. Severini, Thomas A. & Tripathi, Gautam, 2006. "Some Identification Issues In Nonparametric Linear Models With Endogenous Regressors," Econometric Theory, Cambridge University Press, vol. 22(2), pages 258-278, April.
    10. Victor A. Lapshin & Vadim Ya. Kaushanskiy, 2014. "A Nonparametric Method For Term Structure Fitting With Automatic Smoothing," HSE Working papers WP BRP 39/FE/2014, National Research University Higher School of Economics.
    11. Lorenčič Eva, 2016. "Testing the Performance of Cubic Splines and Nelson-Siegel Model for Estimating the Zero-coupon Yield Curve," Naše gospodarstvo/Our economy, Sciendo, vol. 62(2), pages 42-50, June.
    12. Andrew Jeffrey & Oliver Linton & Thong Nguyen, 2001. "Flexible Term Structure Estimation: Which Method Is Preferred?," Yale School of Management Working Papers ysm171, Yale School of Management, revised 01 Oct 2001.
    13. Dennis Schroers, 2024. "Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions," Papers 2401.16286, arXiv.org.
    14. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    15. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    16. Ivailo Arsov & Matthew Brooks & Mitch Kosev, 2013. "New Measures of Australian Corporate Credit Spreads," RBA Bulletin (Print copy discontinued), Reserve Bank of Australia, pages 15-26, December.
    17. Tatyana Krivobokova & Göran Kauermann & Theofanis Archontakis, 2006. "Estimating the term structure of interest rates using penalized splines," Statistical Papers, Springer, vol. 47(3), pages 443-459, June.
    18. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.
    19. Cai, Junyang & Zhou, Jian, 2022. "How many asymptomatic cases were unconfirmed in the US COVID-19 pandemic? The evidence from a serological survey," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    20. Michiel De Pooter, 2007. "Examining the Nelson-Siegel Class of Term Structure Models," Tinbergen Institute Discussion Papers 07-043/4, Tinbergen Institute.
    21. Hiroyuki Kawakatsu, 2020. "Recovering Yield Curves from Dynamic Term Structure Models with Time-Varying Factors," Stats, MDPI, vol. 3(3), pages 1-46, August.

  117. Oliver Linton & Gregory Connor, 2000. "Semiparametric Estimation of a Characteristic-Based Factor Model of Stock Returns," FMG Discussion Papers dp346, Financial Markets Group.

    Cited by:

    1. Matthias Fengler & Wolfgang Härdle & Enno Mammen, 2005. "A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics," SFB 649 Discussion Papers SFB649DP2005-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

  118. Nielsen, Jens P. & Linton, Oliver & Bickel, Peter J., 1998. "On a semiparametric survival model with flexible covariate effect," LSE Research Online Documents on Economics 301, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. van den Berg, Gerard. J. & Janys, Lena & Mammen, Enno & Nielsen, Jens Perch, 2021. "A general semiparametric approach to inference with marker-dependent hazard rate models," Journal of Econometrics, Elsevier, vol. 221(1), pages 43-67.
    2. Xuewen Lu & Jie Sun & Yongcheng Qi, 2008. "Empirical likelihood for average derivatives of hazard regression functions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 67(1), pages 93-112, January.
    3. Lu, Xuewen, 2010. "Asymptotic distributions of two "synthetic data" estimators for censored single-index models," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 999-1015, April.
    4. Tang, Xingyu & Li, Jianbo & Lian, Heng, 2013. "Empirical likelihood for partially linear proportional hazards models with growing dimensions," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 22-32.
    5. Spierdijk, Laura, 2008. "Nonparametric conditional hazard rate estimation: A local linear approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2419-2434, January.
    6. Li, Jianbo & Zhang, Riquan, 2011. "Partially varying coefficient single index proportional hazards regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 389-400, January.
    7. Sun, Jie & Kopciuk, Karen A. & Lu, Xuewen, 2008. "Polynomial spline estimation of partially linear single-index proportional hazards regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 176-188, September.

  119. Oliver Linton & E. Mammen & J. Nielsen & C. Tanggaard, 1998. "Estimating Yield Curves by Kernel Smoothing Methods," Cowles Foundation Discussion Papers 1205, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Andrew Jeffrey & Linton, Oliver Linton & Thong Nguyen & Peter C.B. Phillips, 2001. "Nonparametric Estimation of a Multifactor Heath-Jarrow-Morton Model: An Integrated Approach," Cowles Foundation Discussion Papers 1311, Cowles Foundation for Research in Economics, Yale University.

  120. Sperlich, Stefan & Hardle, Wolfgang & Linton, Oliver, 1998. "Integration and Backfitting methods in additive models: finite sample properties and comparison," DES - Working Papers. Statistics and Econometrics. WS 6270, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Sukjin Han, 2012. "Nonparametric Estimation of Triangular Simultaneous Equations Models under Weak Identification," Department of Economics Working Papers 140414, The University of Texas at Austin, Department of Economics, revised Apr 2014.
    2. Graciela Boente & Alejandra Martínez, 2017. "Marginal integration M-estimators for additive models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 231-260, June.
    3. Härdle Wolfgang Karl & Silyakova Elena, 2016. "Implied basket correlation dynamics," Statistics & Risk Modeling, De Gruyter, vol. 33(1-2), pages 1-20, September.
    4. Scholz, Michael & Sperlich, Stefan & Nielsen, Jens Perch, 2016. "Nonparametric long term prediction of stock returns with generated bond yields," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 82-96.
    5. Berthold R. Haag, 2008. "Non‐parametric Regression Tests Using Dimension Reduction Techniques," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 719-738, December.
    6. Grasshoff, Ulrike & Schwalbach, Joachim & Sperlich, Stefan, 1999. "Executive pay and corporate financial performance. An exploratiove data analysis," DES - Working Papers. Statistics and Econometrics. WS 6382, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    8. Guo, Zheng-Feng & Shintani, Mototsugu, 2011. "Nonparametric lag selection for nonlinear additive autoregressive models," Economics Letters, Elsevier, vol. 111(2), pages 131-134, May.
    9. Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2014. "Testing for additivity in partially linear regression with possibly missing responses," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 51-61.
    10. Graciela Boente & Alejandra Martínez & Matías Salibián-Barrera, 2017. "Robust estimators for additive models using backfitting," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 744-767, October.
    11. Rodriguez Poo, Juan M. & Sperlich, Stefan & Vieu, Philippe, 2000. "Semiparametric estimation of weak and strong separable models," DES - Working Papers. Statistics and Econometrics. WS 10064, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Hengartner, Nicolas W. & Sperlich, Stefan, 2005. "Rate optimal estimation with the integration method in the presence of many covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 246-272, August.
    13. Martins-Filho, Carlos & yang, ke, 2007. "Finite sample performance of kernel-based regression methods for non-parametric additive models under common bandwidth selection criterion," MPRA Paper 39295, University Library of Munich, Germany.
    14. Stefan Sperlich, 2014. "On the choice of regularization parameters in specification testing: a critical discussion," Empirical Economics, Springer, vol. 47(2), pages 427-450, September.
    15. Fernández, Ana I. & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 1998. "Semiparametric three step estimation methods in labor supply models," SFB 373 Discussion Papers 1998,71, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    16. Yang, Lijian & Sperlich, Stefan & Hardle, Wolfgang, 2000. "Derivative estimation and testing in generalized additive models," DES - Working Papers. Statistics and Econometrics. WS 10084, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Härdle, Wolfgang & Huet, Sylvie & Mammen, Enno & Sperlich, Stefan, 2004. "Bootstrap Inference In Semiparametric Generalized Additive Models," Econometric Theory, Cambridge University Press, vol. 20(2), pages 265-300, April.
    18. Michael Scholz & Stefan Sperlich & Jens Perch Nielsen, 2012. "Nonparametric prediction of stock returns with generated bond yields," Graz Economics Papers 2012-10, University of Graz, Department of Economics.
    19. Su, Liangjun & Ullah, Aman, 2008. "Local polynomial estimation of nonparametric simultaneous equations models," Journal of Econometrics, Elsevier, vol. 144(1), pages 193-218, May.
    20. Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and predicting the distribution of the number of visits to the medical doctor," MAGKS Papers on Economics 201148, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    21. Wolfgang Karl Hardle & Elena Silyakova, 2020. "Implied Basket Correlation Dynamics," Papers 2009.09770, arXiv.org.

  121. Arthur Lewbel & Linton, Oliver Linton, 1998. "Nonparametric Censored Regression," Cowles Foundation Discussion Papers 1186, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Richard W. Blundell & James L. Powell, 2004. "Endogeneity in Semiparametric Binary Response Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(3), pages 655-679.
    2. Chen, Songnian & Khan, Shakeeb, 2000. "Estimating censored regression models in the presence of nonparametric multiplicative heteroskedasticity," Journal of Econometrics, Elsevier, vol. 98(2), pages 283-316, October.

  122. Oliver Linton & E. Mammen & J. Nielsen, 1997. "The Existence and Asymptotic Properties of a Backfitting Projection Algorithm Under Weak Conditions," Cowles Foundation Discussion Papers 1160, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Dong, Chaohua & Linton, Oliver, 2018. "Additive nonparametric models with time variable and both stationary and nonstationary regressors," Journal of Econometrics, Elsevier, vol. 207(1), pages 212-236.
    2. Horowitz, Joel L., 2004. "Semiparametric models," Papers 2004,17, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    3. Wang, Li & Wang, Suojin, 2011. "Nonparametric additive model-assisted estimation for survey data," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1126-1140, August.
    4. Oliver B. Linton & Enno Mammen & J. Nielsen & Carsten Tanggaard, 2000. "Yield Curve Estimation by Kernel Smoothing Methods," Econometric Society World Congress 2000 Contributed Papers 0235, Econometric Society.
    5. Lee, Kyeongeun & Lee, Young K. & Park, Byeong U. & Yang, Seong J., 2018. "Time-dynamic varying coefficient models for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 50-65.
    6. Oliver Linton & Gregory Connor, 2000. "Semiparametric Estimation of a Characteristic-Based Factor Model of Stock Returns," FMG Discussion Papers dp346, Financial Markets Group.
    7. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers CWP20/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Song, Qiongxia & Yang, Lijian, 2010. "Oracally efficient spline smoothing of nonlinear additive autoregression models with simultaneous confidence band," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2008-2025, October.
    9. Enno Mammen & Christoph Rothe & Melanie Schienle, 2012. "Generated Covariates in Nonparametric Estimation: A Short Review," SFB 649 Discussion Papers SFB649DP2012-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Yang, Lijian & Park, Byeong U. & Xue, Lan & Hardle, Wolfgang, 2006. "Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1212-1227, September.
    11. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Gagliardini, Patrick & Scaillet, Olivier, 2012. "Tikhonov regularization for nonparametric instrumental variable estimators," Journal of Econometrics, Elsevier, vol. 167(1), pages 61-75.
    13. Fengler, M.R. & Mammen, E. & Vogt, M., 2015. "Specification and structural break tests for additive models with applications to realized variance data," Journal of Econometrics, Elsevier, vol. 188(1), pages 196-218.
    14. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    15. Vanhems, Anne & Van Keilegom, Ingrid, 2013. "Semiparametric transformation model with endogeneity: a control function approach," LIDAM Discussion Papers ISBA 2013018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    17. Gao, Jiti & Lu, Zudi & Tjostheim, Dag, 2003. "Estimation in semiparametric spatial regression," MPRA Paper 11979, University Library of Munich, Germany, revised Jul 2005.
    18. Linton, Oliver B. & Mammen, Enno, 2008. "Nonparametric transformation to white noise," Journal of Econometrics, Elsevier, vol. 142(1), pages 241-264, January.
    19. Raouf, BOUCEKKINE & Bity, DIENE & Théophile, AZOMAHOU, 2007. "A closer look at the relationship between life expectancy and economic growth," Discussion Papers (ECON - Département des Sciences Economiques) 2007043, Université catholique de Louvain, Département des Sciences Economiques.
    20. Peroni, Chiara, 2007. "A non-parametric investigation of risk premia," MPRA Paper 5126, University Library of Munich, Germany, revised 01 Dec 2007.
    21. Hao Dong & Taisuke Otsu, 2018. "Nonparametric Estimation of Additive Model With Errors-in-Variables," Departmental Working Papers 1812, Southern Methodist University, Department of Economics.
    22. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    23. Wolfgang Haerdle & Oliver Linton & Qihua Wang, 2003. "Semiparametric Regression Analysis under Imputation for Missing Response Data," STICERD - Econometrics Paper Series 454, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    24. Suphi Sen & Bertrand Melenberg & Herman R. J. Vollebergh, 2016. "Identification and Estimation of the Environmental Kuznets Curve: Pairwise Differencing to Deal with Nonlinearity and Nonstationarity," CESifo Working Paper Series 5837, CESifo.
    25. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    26. Rüdiger Krause & Gerhard Tutz, 2006. "Genetic algorithms for the selection of smoothing parameters in additive models," Computational Statistics, Springer, vol. 21(1), pages 9-31, March.
    27. Green, Carl & Long, Wei & Hsiao, Cheng, 2015. "Testing error serial correlation in fixed effects nonparametric panel data models," Journal of Econometrics, Elsevier, vol. 188(2), pages 466-473.
    28. Berthold R. Haag, 2008. "Non‐parametric Regression Tests Using Dimension Reduction Techniques," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 719-738, December.
    29. Suneel Babu Chatla, 2023. "Nonparametric inference for additive models estimated via simplified smooth backfitting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 71-97, February.
    30. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers 02/13, Institute for Fiscal Studies.
    31. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    32. Lin, Lu & Song, Yunquan & Liu, Zhao, 2014. "Local linear–additive estimation for multiple nonparametric regressions," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 252-269.
    33. Grasshoff, Ulrike & Schwalbach, Joachim & Sperlich, Stefan, 1999. "Executive pay and corporate financial performance. An exploratiove data analysis," DES - Working Papers. Statistics and Econometrics. WS 6382, Universidad Carlos III de Madrid. Departamento de Estadística.
    34. Gayle, Wayne-Roy & Namoro, Soiliou Daw, 2013. "Estimation of a nonlinear panel data model with semiparametric individual effects," Journal of Econometrics, Elsevier, vol. 175(1), pages 46-59.
    35. Hao Dong & Taisuke Otsu & Luke Taylor, 2022. "Nonparametric estimation of additive models with errors-in-variables," Econometric Reviews, Taylor & Francis Journals, vol. 41(10), pages 1164-1204, November.
    36. Ozabaci, Deniz & Henderson, Daniel J., 2014. "Additive Kernel Estimates of Returns to Schooling," IZA Discussion Papers 8736, Institute of Labor Economics (IZA).
    37. Li, Qi & Hsiao, Cheng & Zinn, Joel, 2003. "Consistent specification tests for semiparametric/nonparametric models based on series estimation methods," Journal of Econometrics, Elsevier, vol. 112(2), pages 295-325, February.
    38. Huang, Zhensheng & Zhang, Riquan, 2009. "Efficient estimation of adaptive varying-coefficient partially linear regression model," Statistics & Probability Letters, Elsevier, vol. 79(7), pages 943-952, April.
    39. Rui Li & Yuanyuan Zhang, 2021. "Two-stage estimation and simultaneous confidence band in partially nonlinear additive model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(8), pages 1109-1140, November.
    40. Enno Mammen & Christoph Rothe & Melanie Schienle, 2010. "Nonparametric Regression with Nonparametrically Generated Covariates," SFB 649 Discussion Papers SFB649DP2010-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    41. Tu, Yundong & Wang, Ying, 2022. "Spurious functional-coefficient regression models and robust inference with marginal integration," Journal of Econometrics, Elsevier, vol. 229(2), pages 396-421.
    42. Sperlich, Stefan & Tjostheim, Dag & Yang, Lijian, 1999. "Nonparametric estimation and testing of interaction in additive models," DES - Working Papers. Statistics and Econometrics. WS 6387, Universidad Carlos III de Madrid. Departamento de Estadística.
    43. Häggström, Jenny, 2013. "Bandwidth selection for backfitting estimation of semiparametric additive models: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 136-148.
    44. Holger Dette & Matthias Guhlich & Natalie Neumeyer, 2015. "Testing for additivity in nonparametric quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(3), pages 437-477, June.
    45. Van Keilegom, Ingrid & Vanhems, Anne, 2011. "Semiparametric transformation model with endogeneity: a control function approach," TSE Working Papers 11-243, Toulouse School of Economics (TSE).
    46. Joel L. Horowitz & Enno Mammen, 2002. "Nonparametric estimation of an additive model with a link function," CeMMAP working papers 19/02, Institute for Fiscal Studies.
    47. Guo, Zheng-Feng & Shintani, Mototsugu, 2011. "Nonparametric lag selection for nonlinear additive autoregressive models," Economics Letters, Elsevier, vol. 111(2), pages 131-134, May.
    48. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    49. Kim, Woocheol & Linton, Oliver, 2004. "A local instrumental variable estimation method for generalized additive volatility models," LSE Research Online Documents on Economics 24758, London School of Economics and Political Science, LSE Library.
    50. Linton, Oliver & Sancetta, Alessio, 2009. "Consistent estimation of a general nonparametric regression function in time series," Journal of Econometrics, Elsevier, vol. 152(1), pages 70-78, September.
    51. Abhijit Mandal, 2020. "An optimal test for the additive model with discrete or categorical predictors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(6), pages 1397-1417, December.
    52. Dette, Holger & von Lieres und Wilkau, Carsten, 2000. "Testing additivity by kernel based methods - what is a reasonable test?," Technical Reports 2000,39, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    53. Stefan Sperlich & Oliver Linton & Wolfgang Härdle, 1999. "Integration and backfitting methods in additive models-finite sample properties and comparison," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 419-458, December.
    54. Profit, Stefan & Sperlich, Stefan, 1999. "Non-uniformity of job-matching in a transition economy- a nonparametric analysis for the czech republic," DES - Working Papers. Statistics and Econometrics. WS 6287, Universidad Carlos III de Madrid. Departamento de Estadística.
    55. Fengler, Matthias R. & Mammen, Enno & Vogt, Michael, 2013. "Additive modeling of realized variance: tests for parametric specifications and structural breaks," Economics Working Paper Series 1332, University of St. Gallen, School of Economics and Political Science.
    56. Oliver Linton & E. Mammen & J. Nielsen & C. Tanggaard, 1998. "Estimating Yield Curves by Kernel Smoothing Methods," Cowles Foundation Discussion Papers 1205, Cowles Foundation for Research in Economics, Yale University.
    57. Fei Liu & Jiti Gao & Yanrong Yang, 2020. "Time-Varying Panel Data Models with an Additive Factor Structure," Monash Econometrics and Business Statistics Working Papers 42/20, Monash University, Department of Econometrics and Business Statistics.
    58. Stefan Sperlich & Raoul Theler, 2015. "Modeling heterogeneity: a praise for varying-coefficient models in causal analysis," Computational Statistics, Springer, vol. 30(3), pages 693-718, September.
    59. Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2014. "Testing for additivity in partially linear regression with possibly missing responses," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 51-61.
    60. Woocheol Kim, 2004. "Identification And Estimation Of Nonparametric Structural," Econometric Society 2004 Far Eastern Meetings 733, Econometric Society.
    61. Linton, Oliver & Mammen, Enno, 2004. "Estimating semiparametric ARCH (∞) models by kernel smoothing methods," LSE Research Online Documents on Economics 24762, London School of Economics and Political Science, LSE Library.
    62. Zhengyan Lin & Degui Li & Jiti Gao, 2009. "Local Linear M‐estimation in non‐parametric spatial regression," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages 286-314, May.
    63. Joel L. Horowitz & Enno Mammen, 2002. "Nonparametric estimation of an additive model with a link function," CeMMAP working papers CWP19/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    64. O. Linton & E. Mammen, 2005. "Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods," Econometrica, Econometric Society, vol. 73(3), pages 771-836, May.
    65. Vanhems, Anne & Van Keilegom, Ingrid, 2011. "Semiparametric transformation model with endogeneity: a control function approach," LIDAM Discussion Papers ISBA 2011011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    66. Li, Cong & Liang, Zhongwen, 2015. "Asymptotics for nonparametric and semiparametric fixed effects panel models," Journal of Econometrics, Elsevier, vol. 185(2), pages 420-434.
    67. Jing Wang & Lijian Yang, 2009. "Efficient and fast spline-backfitted kernel smoothing of additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 663-690, September.
    68. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    69. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    70. Rodriguez Poo, Juan M. & Sperlich, Stefan & Vieu, Philippe, 2000. "Semiparametric estimation of weak and strong separable models," DES - Working Papers. Statistics and Econometrics. WS 10064, Universidad Carlos III de Madrid. Departamento de Estadística.
    71. Hengartner, Nicolas W. & Sperlich, Stefan, 2005. "Rate optimal estimation with the integration method in the presence of many covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 246-272, August.
    72. Martins-Filho, Carlos & yang, ke, 2007. "Finite sample performance of kernel-based regression methods for non-parametric additive models under common bandwidth selection criterion," MPRA Paper 39295, University Library of Munich, Germany.
    73. Schimek, Michael G. & Turlach, Berwin A., 1998. "Additive and generalized additive models: A survey," SFB 373 Discussion Papers 1998,97, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    74. Mammen, Enno & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2015. "In-sample forecasting applied to reserving and mesothelioma mortality," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 76-86.
    75. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers 20/12, Institute for Fiscal Studies.
    76. Byeong U. Park & Enno Mammen & Young K. Lee & Eun Ryung Lee, 2015. "Varying Coefficient Regression Models: A Review and New Developments," International Statistical Review, International Statistical Institute, vol. 83(1), pages 36-64, April.
    77. Stefan Sperlich, 2014. "On the choice of regularization parameters in specification testing: a critical discussion," Empirical Economics, Springer, vol. 47(2), pages 427-450, September.
    78. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    79. Vanhems, Anne & Van Keilegom, Ingrid, 2019. "Estimation Of A Semiparametric Transformation Model In The Presence Of Endogeneity," Econometric Theory, Cambridge University Press, vol. 35(1), pages 73-110, February.
    80. Fernández, Ana I. & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 1998. "Semiparametric three step estimation methods in labor supply models," SFB 373 Discussion Papers 1998,71, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    81. Xia Cui & Heng Peng & Songqiao Wen & Lixing Zhu, 2013. "Component Selection in the Additive Regression Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 491-510, September.
    82. Carroll, Raymond J. & Härdle, Wolfgang & Mammen, Enno, 1999. "Estimation in an additive model when the components are linked parametrically," SFB 373 Discussion Papers 1999,1, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    83. Mammen, Enno & Martínez-Miranda, María Dolores & Nielsen, Jens Perch & Vogt, Michael, 2021. "Calendar effect and in-sample forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 31-52.
    84. Yang, Lijian & Sperlich, Stefan & Hardle, Wolfgang, 2000. "Derivative estimation and testing in generalized additive models," DES - Working Papers. Statistics and Econometrics. WS 10084, Universidad Carlos III de Madrid. Departamento de Estadística.
    85. Härdle, Wolfgang & Huet, Sylvie & Mammen, Enno & Sperlich, Stefan, 2004. "Bootstrap Inference In Semiparametric Generalized Additive Models," Econometric Theory, Cambridge University Press, vol. 20(2), pages 265-300, April.
    86. Henderson, Daniel J. & Carroll, Raymond J. & Li, Qi, 2008. "Nonparametric estimation and testing of fixed effects panel data models," Journal of Econometrics, Elsevier, vol. 144(1), pages 257-275, May.
    87. Lin, Huazhen & Pan, Lixian & Lv, Shaogao & Zhang, Wenyang, 2018. "Efficient estimation and computation for the generalised additive models with unknown link function," Journal of Econometrics, Elsevier, vol. 202(2), pages 230-244.
    88. Qian Huang & Jinhong You & Liwen Zhang, 2022. "Efficient inference of longitudinal/functional data models with time‐varying additive structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 744-771, June.
    89. Han, Kyunghee & Lee, Young K. & Park, Byeong U., 2020. "Smooth backfitting for errors-in-variables varying coefficient regression models," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    90. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.
    91. Maria Grith & Wolfgang Karl Härdle & Melanie Schienle, 2010. "Nonparametric Estimation of Risk-Neutral Densities," SFB 649 Discussion Papers SFB649DP2010-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    92. Gutknecht, Daniel, 2012. "Do Reservation Wages Decline Monotonically? A Novel Statistical Test," Economic Research Papers 270635, University of Warwick - Department of Economics.
    93. Andrew Jeffrey, 2004. "Nonparametric Estimation of a Multifactor Heath-Jarrow-Morton Model: An Integrated Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 251-289.
    94. Lu, Zudi & Lundervold, Arvid & Tjøstheim, Dag & Yao, Qiwei, 2007. "Exploring spatial nonlinearity using additive approximation," LSE Research Online Documents on Economics 5401, London School of Economics and Political Science, LSE Library.
    95. Hu, Lixia & Huang, Tao & You, Jinhong, 2019. "Two-step estimation of time-varying additive model for locally stationary time series," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 94-110.
    96. Juhyun Park & Burkhardt Seifert, 2010. "Local additive estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 171-191, March.
    97. Horowitz, Joel L. & Mammen, Enno, 2002. "Nonparametric estimation of an additive model with a link function," SFB 373 Discussion Papers 2002,63, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    98. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.

  123. Oliver Linton, 1997. "Second Order Approximation in a Linear Regression with Heteroskedasticity for Unknown Form," Cowles Foundation Discussion Papers 1151, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Oliver Linton & Douglas G. Steigerwald, 1995. "Adaptive Testing in ARCH Models," Cowles Foundation Discussion Papers 1105, Cowles Foundation for Research in Economics, Yale University.
    2. Peter C.B. Phillips & Binbin Guo & Zhijie Xiao, 2002. "Efficient Regression in Time Series Partial Linear Models," Cowles Foundation Discussion Papers 1363, Cowles Foundation for Research in Economics, Yale University.

  124. Yanqin Fan & Oliver Linton, 1997. "Some Higher Order Theory for a Consistent Nonparametric Model Specification Test," Cowles Foundation Discussion Papers 1148, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Fernandes, Marcelo, 2001. "Nonparametric entropy-based tests of independence between stochastic processes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 413, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Marcelo Fernandes & Joachim Grammig, 2000. "Non-Parametric Specification Tests For Conditional Duration Models," Computing in Economics and Finance 2000 40, Society for Computational Economics.
    3. Oliver Linton & Pedro Gozalo, 1995. "Testing Additivity in Generalized Nonparametric Regression Models," Cowles Foundation Discussion Papers 1106, Cowles Foundation for Research in Economics, Yale University.
    4. Gao, Jiti & Casas, Isabel, 2006. "Specification testing in discretized diffusion models: Theory and practice," MPRA Paper 11980, University Library of Munich, Germany, revised Aug 2007.
    5. Patrick Marsh, "undated". "Nonparametric Likelihood Ratio Tests," Discussion Papers 00/56, Department of Economics, University of York.
    6. Joao Amaro de Matos & Marcelo Fernandes, 2004. "Testing the Markov property with ultra-high frequency financial data," Nova SBE Working Paper Series wp462, Universidade Nova de Lisboa, Nova School of Business and Economics.
    7. Gao, Jiti & Gijbels, Irene, 2005. "Bandwidth selection for nonparametric kernel testing," MPRA Paper 11982, University Library of Munich, Germany, revised Jun 2007.

  125. Sperlich, S. & Linton, O. & Härdle, Wolfgang, 1997. "A Simulation Comparison between Integration and Backfitting Methods of Estimating Separable Nonparametric Regression Models," SFB 373 Discussion Papers 1997,66, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Gozalo, Pedro L. & Linton, Oliver B., 2001. "Testing additivity in generalized nonparametric regression models with estimated parameters," Journal of Econometrics, Elsevier, vol. 104(1), pages 1-48, August.
    2. Profit, Stefan & Sperlich, Stefan, 1999. "Non-uniformity of job-matching in a transition economy- a nonparametric analysis for the czech republic," DES - Working Papers. Statistics and Econometrics. WS 6287, Universidad Carlos III de Madrid. Departamento de Estadística.

  126. Yoon-Jae Whang & Oliver Linton, 1997. "The Asymptotic Distribution of Nonparametric Estimates of the Lyapunov Exponent for Stochastic Time Series," Cowles Foundation Discussion Papers 1130R, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. J. Barkley Rosser, 1999. "On the Complexities of Complex Economic Dynamics," Journal of Economic Perspectives, American Economic Association, vol. 13(4), pages 169-192, Fall.
    2. Park, Joon Y. & Whang, Yoon-Jae, 2012. "Random walk or chaos: A formal test on the Lyapunov exponent," Journal of Econometrics, Elsevier, vol. 169(1), pages 61-74.
    3. Bask, Mikael & de Luna, Xavier, 2001. "Characterizing the degree of stability of non-linear dynamic models," Umeå Economic Studies 564, Umeå University, Department of Economics.
    4. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    5. Kyrtsou, Catherine & Serletis, Apostolos, 2006. "Univariate tests for nonlinear structure," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 154-168, March.
    6. Park, Joon Y. & Whang, Yoon-Jae, 2004. "A Test of the Martingale Hypothesis," Working Papers 2004-11, Rice University, Department of Economics.
    7. Serletis, Apostolos & He, Mingyu & Chowdhury, M.M. Islam, 2023. "Chaos in long-maturity real rates," Economics Letters, Elsevier, vol. 225(C).
    8. Shintani, Mototsugu, 2008. "A dynamic factor approach to nonlinear stability analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 32(9), pages 2788-2808, September.
    9. Kian‐Ping Lim & Robert Brooks, 2011. "The Evolution Of Stock Market Efficiency Over Time: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 69-108, February.
    10. Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero & Julián Andrada-Félix, "undated". "Testing Chaotic Dynamics via Lyapunov Exponents," Working Papers 2000-07, FEDEA.
    11. Sandubete, Julio E. & Escot, Lorenzo, 2020. "Chaotic signals inside some tick-by-tick financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    12. Jorge Belaire-Franch & Kwaku Opong, 2013. "A Time Series Analysis of U.K. Construction and Real Estate Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 46(3), pages 516-542, April.
    13. Oliver Linton & Mototsugu Shintani, 2001. "Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors," FMG Discussion Papers dp383, Financial Markets Group.
    14. Serletis, Apostolos & Shintani, Mototsugu, 2006. "Chaotic monetary dynamics with confidence," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 228-252, March.
    15. Shintani, Mototsugu & Linton, Oliver, 2002. "Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos," LSE Research Online Documents on Economics 2093, London School of Economics and Political Science, LSE Library.
    16. Orzeszko, Witold, 2008. "The new method of measuring the effects of noise reduction in chaotic data," Chaos, Solitons & Fractals, Elsevier, vol. 38(5), pages 1355-1368.
    17. Charles-Cadogan, G., 2021. "Market Instability, Investor Sentiment, And Probability Judgment Error in Index Option Prices," CRETA Online Discussion Paper Series 71, Centre for Research in Economic Theory and its Applications CRETA.
    18. Serletis, Apostolos & Shahmoradi, Asghar, 2007. "Chaos, self-organized criticality, and SETAR nonlinearity: An analysis of purchasing power parity between Canada and the United States," Chaos, Solitons & Fractals, Elsevier, vol. 33(5), pages 1437-1444.
    19. Wolff, Rodney C. & Yao, Qiwei & Tong, Howell, 2004. "Statistical tests for Lyapunov exponents of deterministic systems," LSE Research Online Documents on Economics 154, London School of Economics and Political Science, LSE Library.
    20. Escot, Lorenzo & Sandubete, Julio E., 2023. "Estimating Lyapunov exponents on a noisy environment by global and local Jacobian indirect algorithms," Applied Mathematics and Computation, Elsevier, vol. 436(C).
    21. Bask, Mikael & de Luna, Xavier, 2001. "EMU and the Stability and Volatility of Foreign Exchange: Some Empirical Evidence," Umeå Economic Studies 565, Umeå University, Department of Economics.
    22. Serletis, Apostolos & Uritskaya, Olga Y., 2007. "Detecting signatures of stochastic self-organization in US money and velocity measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 281-291.
    23. Giannerini Simone & Rosa Rodolfo, 2004. "Assessing Chaos in Time Series: Statistical Aspects and Perspectives," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-25, May.
    24. Serletis, Apostolos & Shahmoradi, Asghar & Serletis, Demitre, 2007. "Effect of noise on estimation of Lyapunov exponents from a time series," Chaos, Solitons & Fractals, Elsevier, vol. 32(2), pages 883-887.
    25. Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero & Julián Andrada-Félix, "undated". "A New Test for Chaotic Dynamics Using Lyapunov Exponents," Working Papers 2003-09, FEDEA.

  127. Oliver Linton & Pedro Gozalo, 1996. "Conditional Independence Restrictions: Testing and Estimation," Cowles Foundation Discussion Papers 1140, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers CWP24/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers 24/12, Institute for Fiscal Studies.
    3. Joeri Smits & Jeffrey S. Racine, 2013. "Testing Exclusion Restrictions in Nonseparable Triangular Models," Department of Economics Working Papers 2013-02, McMaster University.
    4. Wooldridge, Jeffrey M. & Imbens, Guido, 2009. "Recent Developments in the Econometrics of Program Evaluation," Scholarly Articles 3043416, Harvard University Department of Economics.
    5. Poirier, Alexandre, 2017. "Efficient estimation in models with independence restrictions," Journal of Econometrics, Elsevier, vol. 196(1), pages 1-22.
    6. Joao Amaro de Matos & Marcelo Fernandes, 2004. "Testing the Markov property with ultra-high frequency financial data," Nova SBE Working Paper Series wp462, Universidade Nova de Lisboa, Nova School of Business and Economics.
    7. Cheng, Yu-Hsiang & Huang, Tzee-Ming, 2012. "A conditional independence test for dependent data based on maximal conditional correlation," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 210-226.
    8. Yanqin Fan & Oliver Linton, 1997. "Some Higher Order Theory for a Consistent Nonparametric Model Specification Test," Cowles Foundation Discussion Papers 1148, Cowles Foundation for Research in Economics, Yale University.
    9. Györfi, László & Walk, Harro, 2012. "Strongly consistent nonparametric tests of conditional independence," Statistics & Probability Letters, Elsevier, vol. 82(6), pages 1145-1150.

  128. Nielsen, J. P. & Linton, O. B., 1996. "An Optimization Interpretation of Integration and Backfitting Estimators for Separable Nonparametric Models," SFB 373 Discussion Papers 1996,88, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Oliver B. Linton & Enno Mammen & J. Nielsen & Carsten Tanggaard, 2000. "Yield Curve Estimation by Kernel Smoothing Methods," Econometric Society World Congress 2000 Contributed Papers 0235, Econometric Society.
    2. Graciela Boente & Alejandra Martínez, 2017. "Marginal integration M-estimators for additive models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 231-260, June.
    3. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    4. Abe, Makoto & Boztuæg, Yasemin & Hildebrandt, Lutz, 2000. "Investigation of the stochastic utility maximization process of consumer brand choice by semiparametric modeling," SFB 373 Discussion Papers 2000,84, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Nathalie Chèze & Jean-Michel Poggi & Bruno Portier, 2003. "Partial and Recombined Estimators for Nonlinear Additive Models," Statistical Inference for Stochastic Processes, Springer, vol. 6(2), pages 155-197, May.
    6. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Li, Qi & Hsiao, Cheng & Zinn, Joel, 2003. "Consistent specification tests for semiparametric/nonparametric models based on series estimation methods," Journal of Econometrics, Elsevier, vol. 112(2), pages 295-325, February.
    8. Makoto Abe & Yasemin Boztug & Lutz Hildebrandt, 2003. "Investigating the Competitive Assumption of Multinomial Logit Models of Brand Choice by Nonparametric Modeling," CIRJE F-Series CIRJE-F-193, CIRJE, Faculty of Economics, University of Tokyo.
    9. Oliver Linton & E. Mammen & J. Nielsen & C. Tanggaard, 1998. "Estimating Yield Curves by Kernel Smoothing Methods," Cowles Foundation Discussion Papers 1205, Cowles Foundation for Research in Economics, Yale University.
    10. Fei Liu & Jiti Gao & Yanrong Yang, 2020. "Time-Varying Panel Data Models with an Additive Factor Structure," Monash Econometrics and Business Statistics Working Papers 42/20, Monash University, Department of Econometrics and Business Statistics.
    11. Lawrence Dacuycuy, 2006. "Explaining male wage inequality in the Philippines: non-parametric and semiparametric approaches," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2497-2511.
    12. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    13. Mammen, Enno & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2015. "In-sample forecasting applied to reserving and mesothelioma mortality," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 76-86.
    14. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.

  129. Oliver Linton, 1996. "An Asymptotic Expansion in the Garch(1,1) Model," Cowles Foundation Discussion Papers 1118, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Emma M. Iglesias & Garry D. A. Phillips, 2012. "Estimation, Testing, and Finite Sample Properties of Quasi-Maximum Likelihood Estimators in GARCH-M Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 532-557, September.
    2. Dietmar P. J. Leisen, 2017. "The shape of small sample biases in pricing kernel estimations," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 943-958, June.
    3. Yong Bao, 2013. "On Sample Skewness and Kurtosis," Econometric Reviews, Taylor & Francis Journals, vol. 32(4), pages 415-448, December.
    4. Rodrigo Alfaro & Carmen Gloria Silva, 2008. "Measuring Equity Volatility: the case of Chilean Stock Index," Working Papers Central Bank of Chile 462, Central Bank of Chile.

  130. Härdle, Wolfgang & Linton, O., 1995. "Nonparametric Regression," SFB 373 Discussion Papers 1995,29, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Severance-Lossin, E. & Sperlich, Stefan, 1997. "Estimation of derivates for additive separable models," SFB 373 Discussion Papers 1997,30, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Arthur Lewbel & Oliver Linton, 2000. "Nonparametric Censored and Truncated Regression," STICERD - Econometrics Paper Series 389, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Wichert, Laura & Wilke, Ralf A., 2005. "Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis," ZEW Discussion Papers 05-67, ZEW - Leibniz Centre for European Economic Research.
    4. Richard W. Blundell & Martin Browning & Ian A. Crawford, 2003. "Nonparametric Engel Curves and Revealed Preference," Econometrica, Econometric Society, vol. 71(1), pages 205-240, January.
    5. Oliver Linton & Pedro Gozalo, 1995. "Testing Additivity in Generalized Nonparametric Regression Models," Cowles Foundation Discussion Papers 1106, Cowles Foundation for Research in Economics, Yale University.
    6. Sperlich, Stefan & Tjostheim, Dag & Yang, Lijian, 1999. "Nonparametric estimation and testing of interaction in additive models," DES - Working Papers. Statistics and Econometrics. WS 6387, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
    8. Profit, Stefan & Sperlich, Stefan, 1999. "Non-uniformity of job-matching in a transition economy- a nonparametric analysis for the czech republic," DES - Working Papers. Statistics and Econometrics. WS 6287, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Härdle, Wolfgang & Huet, Sylvie & Mammen, Enno & Sperlich, Stefan, 1998. "Semiparametric additive indices for binary response and generalized additive models," SFB 373 Discussion Papers 1998,95, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    10. Schimek, Michael G. & Turlach, Berwin A., 1998. "Additive and generalized additive models: A survey," SFB 373 Discussion Papers 1998,97, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    11. Fernández, Ana I. & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 1998. "Semiparametric three step estimation methods in labor supply models," SFB 373 Discussion Papers 1998,71, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    12. Christine A. Ribic & Thomas W. Miller, 1998. "Evaluation of alternative model selection criteria in the analysis of unimodal response curves using CART," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(5), pages 685-698, June.

  131. Linton, O. B. & Härdle, Wolfgang, 1995. "Estimation of Additive Regression Models with Links," SFB 373 Discussion Papers 1995,48, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Yang, Lijian & Härdle, Wolfgang & Nielsen, Jens P., 1998. "Nonparametric autoregression with multiplicative volatility and additive mean," SFB 373 Discussion Papers 1998,107, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Yang, Lijian & Park, Byeong U. & Xue, Lan & Hardle, Wolfgang, 2006. "Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1212-1227, September.
    3. Schimek, Michael G. & Turlach, Berwin A., 1998. "Additive and generalized additive models: A survey," SFB 373 Discussion Papers 1998,97, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    5. Efang Kong & Oliver Linton & Yingcun Xia, 2011. "Global Bahadur representation for nonparametric censored regression quantiles and its applications," CeMMAP working papers CWP33/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Kempe, Wolfram, 1997. "Das Arbeitsangebot verheirateter Frauen in den neuen und alten Bundesländern: Eine semiparametrische Regressionsanalyse," SFB 373 Discussion Papers 1997,3, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    7. Härdle, Wolfgang & Sperlich, Stefan & Spokoiny, Vladimir G., 1997. "Component analysis for additive models," SFB 373 Discussion Papers 1997,52, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  132. Linton, O. B. & Chen, R. & Härdle, Wolfgang, 1995. "An Analysis of Transformations for Additive Nonparanetric Regression," SFB 373 Discussion Papers 1995,68, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Chen, Xiaohong & Linton, Oliver & Van Keilegom, Ingrid, 2003. "Estimation of semiparametric models when the criterion function is not smooth," LSE Research Online Documents on Economics 2167, London School of Economics and Political Science, LSE Library.
    2. Qingliang Fan & Yaqian Wu, 2020. "Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments," Papers 2006.14998, arXiv.org.
    3. Hess, Sebastian & Cramon-Taubadel, Stephan von & Sperlich, 2010. "Numbers for Pascal: Explaining differences in the Estimated Benefited of the Doha Developing Agenda," Department of Agricultural and Rural Development (DARE) Discussion Papers 187311, Georg-August-Universitaet Goettingen, Department of Agricultural Economics and Rural Development (DARE).
    4. Gozalo, Pedro L. & Linton, Oliver B., 2001. "Testing additivity in generalized nonparametric regression models with estimated parameters," Journal of Econometrics, Elsevier, vol. 104(1), pages 1-48, August.
    5. J. S. Allison & M. Hušková & S. G. Meintanis, 2018. "Testing the adequacy of semiparametric transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 70-94, March.
    6. Fryzlewicz, Piotr & Delouille, V´eronique & Nason, Guy P., 2007. "GOES-8 X-ray sensor variance stabilization using the multiscale data-driven Haar-Fisz transform," LSE Research Online Documents on Economics 25221, London School of Economics and Political Science, LSE Library.
    7. Kim, Woocheol & Linton, Oliver, 2004. "A local instrumental variable estimation method for generalized additive volatility models," LSE Research Online Documents on Economics 24758, London School of Economics and Political Science, LSE Library.
    8. Politis, Dimitris N, 2010. "Model-free Model-fitting and Predictive Distributions," University of California at San Diego, Economics Working Paper Series qt67j6s174, Department of Economics, UC San Diego.
    9. Stefan Sperlich & Oliver Linton & Wolfgang Härdle, 1999. "Integration and backfitting methods in additive models-finite sample properties and comparison," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 419-458, December.
    10. Lawrence Dacuycuy, 2006. "Explaining male wage inequality in the Philippines: non-parametric and semiparametric approaches," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2497-2511.

  133. Oliver Linton & Pedro Gozalo, 1995. "Testing Additivity in Generalized Nonparametric Regression Models," Cowles Foundation Discussion Papers 1106, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Joris Pinkse, 2000. "Feasible Multivariate Nonparametric Estimation Using Weak Separability," Econometric Society World Congress 2000 Contributed Papers 1241, Econometric Society.

  134. Chen, R. & Härdle, Wolfgang & Linton, O. B. & Severance-Lossin, E., 1995. "Nonparametric Estimation of Additive Seperable Regression Models," SFB 373 Discussion Papers 1995,50, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Chèze-Payaud, Nathalie & Poggi, Jean-Michel & Portier, Bruno, 1998. "Estimation and test of linearity for a class of additive nonlinear models," Statistics & Probability Letters, Elsevier, vol. 40(2), pages 189-201, September.
    2. Graciela Boente & Alejandra Martínez, 2017. "Marginal integration M-estimators for additive models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 231-260, June.
    3. Avalos, Marta & Grandvalet, Yves & Ambroise, Christophe, 2007. "Parsimonious additive models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2851-2870, March.
    4. Morteza Haghiri & James Nolan & Kien Tran, 2004. "Assessing the impact of economic liberalization across countries: a comparison of dairy industry efficiency in Canada and the USA," Applied Economics, Taylor & Francis Journals, vol. 36(11), pages 1233-1243.
    5. Morteza Haghiri & Alireza Simchi, 2005. "An application of the residual deviance analysis in testing input separability restrictions," Applied Economics Letters, Taylor & Francis Journals, vol. 12(12), pages 755-758.
    6. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    7. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    8. Kempe, Wolfram, 1997. "Das Arbeitsangebot verheirateter Frauen in den neuen und alten Bundesländern: Eine semiparametrische Regressionsanalyse," SFB 373 Discussion Papers 1997,3, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  135. Oliver Linton & Douglas G. Steigerwald, 1995. "Adaptive Testing in ARCH Models," Cowles Foundation Discussion Papers 1105, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Broze, Laurence & Gourieroux, Christian, 1998. "Pseudo-maximum likelihood method, adjusted pseudo-maximum likelihood method and covariance estimators," Journal of Econometrics, Elsevier, vol. 85(1), pages 75-98, July.
    2. Gabriele Fiorentini & Enrique Sentana, 2019. "New testing approaches for mean-variance predictability," Working Paper series 19-01, Rimini Centre for Economic Analysis.
    3. Hodgson, Douglas J & Vorkink, Keith P, 2003. "Efficient Estimation of Conditional Asset-Pricing Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 269-283, April.
    4. Gabriele Fiorentini & Enrique Sentana, 2007. "On the efficiency and consistency of likelihood estimation in multivariate conditionally heteroskedastic dynamic regression models," Working Paper series 38_07, Rimini Centre for Economic Analysis.

  136. Pedro Gozalo & Oliver Linton, 1994. "Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically," Cowles Foundation Discussion Papers 1075, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Arthur Lewbel & Oliver Linton, 2003. "Nonparametric estimation of homothetic and homothetically separable functions," CeMMAP working papers CWP14/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Mittelhammer, Ron C. & Judge, George, 2011. "A family of empirical likelihood functions and estimators for the binary response model," Journal of Econometrics, Elsevier, vol. 164(2), pages 207-217, October.
    3. Bossaerts, P.L.M. & Hillion, P., 1995. "Local Parametric Analysis of Hedging in Discrete Time," Discussion Paper 1995-23, Tilburg University, Center for Economic Research.
    4. Mittelhammer, Ron C Dr. & Judge, George G., 2008. "A Minimum Power Divergence Class of CDFs and Estimators for Binary Choice Models," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt7bc2828q, Department of Agricultural & Resource Economics, UC Berkeley.
    5. Arthur Lewbel, 2007. "Estimation of Average Treatment Effects with Misclassification," Econometrica, Econometric Society, vol. 75(2), pages 537-551, March.
    6. Claudia PIGINI, 2012. "Of Butterflies and Caterpillars: Bivariate Normality in the Sample Selection Model," Working Papers 377, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    7. Rosa Bernardini Papalia, 1999. "Local generalized method of moments estimation based on kernel weights: An application to panel data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 1005-1015.
    8. Gozalo, Pedro L., 1997. "Nonparametric bootstrap analysis with applications to demographic effects in demand functions," Journal of Econometrics, Elsevier, vol. 81(2), pages 357-393, December.
    9. Bossaerts, P.L.M. & Hillion, P., 1995. "Local Parametric Analysis of Hedging in Discrete Time," Other publications TiSEM 77cdfe27-8732-4f09-bf89-f, Tilburg University, School of Economics and Management.
    10. Bossaerts, Peter & Hillion, Pierre, 1997. "Local parametric analysis of hedging in discrete time," Journal of Econometrics, Elsevier, vol. 81(1), pages 243-272, November.
    11. Arthur Lewbel & Oliver Linton, 2003. "Nonparametric Matching and Efficient Estimators of Homothetically Separable Functions," Boston College Working Papers in Economics 585, Boston College Department of Economics, revised 04 Sep 2006.

  137. Wolfgang Hardle & Oliver Linton, 1994. "Applied Nonparametric Methods," Cowles Foundation Discussion Papers 1069, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
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    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
    5. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    6. Miller, Steve & Startz, Richard, 2019. "Feasible generalized least squares using support vector regression," Economics Letters, Elsevier, vol. 175(C), pages 28-31.
    7. Hidehiko Ichimura & Oliver Linton, 2003. "Asymptotic Expansions for Some Semiparametric Program Evaluation Estimators," STICERD - Econometrics Paper Series 451, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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    Cited by:

    1. Su, Liangjun, 2012. "Semiparametric GMM estimation of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 167(2), pages 543-560.
    2. Xiaohong Chen & Demian Pouzo, 2009. "Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals," CeMMAP working papers CWP20/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    4. Xia, Yingcun & Härdle, Wolfgang, 2006. "Semi-parametric estimation of partially linear single-index models," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1162-1184, May.
    5. Dennis Kristensen, 2009. "Semiparametric modelling and estimation (in Russian)," Quantile, Quantile, issue 7, pages 53-83, September.
    6. Liu, Yan & Zhang, Sanguo & Ma, Shuangge & Zhang, Qingzhao, 2020. "Tests for regression coefficients in high dimensional partially linear models," Statistics & Probability Letters, Elsevier, vol. 163(C).
    7. Yingcun Xia & Wolfgang Härdle & Oliver Linton, 2009. "Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator," SFB 649 Discussion Papers SFB649DP2009-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
    9. Hodgson, Douglas J. & Linton, Oliver & Vorkink, Keith, 2004. "Testing forward exchange rate unbiasedness efficiently: A semiparametric approach," Journal of Applied Economics, Universidad del CEMA, vol. 7(2), pages 1-29, November.
    10. Florens, Jean-Pierre & Johannes, Jan & Van Bellegem, Sébastien, 2009. "Instrumental Regression in Partially Linear Models," TSE Working Papers 10-167, Toulouse School of Economics (TSE).
    11. Kotlyarova, Yulia & Schafgans, Marcia M.A. & Zinde-Walsh, Victoria, 2021. "Rates of expansions for functional estimators," LSE Research Online Documents on Economics 113436, London School of Economics and Political Science, LSE Library.
    12. Phillips, Peter C.B., 2014. "Optimal estimation of cointegrated systems with irrelevant instruments," Journal of Econometrics, Elsevier, vol. 178(P2), pages 210-224.
    13. Zhijie Xiao & Peter C.B. Phillips, 1998. "Higher Order Approximations for Wald Statistics in Cointegrating Regressions," Cowles Foundation Discussion Papers 1192, Cowles Foundation for Research in Economics, Yale University.
    14. Byunghoon Kang, 2018. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Working Papers 240829404, Lancaster University Management School, Economics Department.
    15. Qi Li & Aman Ullha, 1998. "Estimating partially linear panel data models with one-way error components," Econometric Reviews, Taylor & Francis Journals, vol. 17(2), pages 145-166.
    16. Wolfgang Haerdle & Oliver Linton & Qihua Wang, 2003. "Semiparametric Regression Analysis under Imputation for Missing Response Data," STICERD - Econometrics Paper Series 454, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    17. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2018. "A Unified Framework for Efficient Estimation of General Treatment Models," Papers 1808.04936, arXiv.org, revised Aug 2018.
    18. Aneiros-Perez, G. & Vilar-Fernandez, J.M., 2008. "Local polynomial estimation in partial linear regression models under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2757-2777, January.
    19. Rothe, Christoph & Firpo, Sergio, 2013. "Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions," IZA Discussion Papers 7564, Institute of Labor Economics (IZA).
    20. Boente, Graciela & Rodriguez, Daniela, 2008. "Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2808-2828, January.
    21. Matias D. Cattaneo & Michael Jansson, 2014. "Bootstrapping Kernel-Based Semiparametric Estimators," CREATES Research Papers 2014-25, Department of Economics and Business Economics, Aarhus University.
    22. Rothe, Christoph, 2016. "The Value of Knowing the Propensity Score for Estimating Average Treatment Effects," IZA Discussion Papers 9989, Institute of Labor Economics (IZA).
    23. Xia, Yingcun & Härdle, Wolfgang, 2002. "Semi-parametric estimation of generalized partially linear single-index models," SFB 373 Discussion Papers 2002,56, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    24. Francesco Bravo & David Jacho-Chavez, 2011. "Empirical Likelihood for Efficient Semiparametric Average Treatment Effects," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 1-24.
    25. Oliver Linton & Mototsugu Shintani, 2001. "Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors," FMG Discussion Papers dp383, Financial Markets Group.
    26. Oliver Linton & Douglas G. Steigerwald, 1995. "Adaptive Testing in ARCH Models," Cowles Foundation Discussion Papers 1105, Cowles Foundation for Research in Economics, Yale University.
    27. Germán Aneiros-Pérez, 2004. "Plug-in bandwidth choice for estimation of nonparametric part in partial linear regression models with strong mixing errors," Statistical Papers, Springer, vol. 45(2), pages 191-210, April.
    28. Hidehiko Ichimura & Oliver Linton, 2003. "Asymptotic Expansions for Some Semiparametric Program Evaluation Estimators," STICERD - Econometrics Paper Series 451, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    29. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
    30. Alexandra Soberon & Irene D’Hers, 2020. "The Environmental Kuznets Curve: A Semiparametric Approach with Cross-Sectional Dependence," JRFM, MDPI, vol. 13(11), pages 1-23, November.
    31. Häggström, Jenny, 2013. "Bandwidth selection for backfitting estimation of semiparametric additive models: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 136-148.
    32. Douglas J. Hodgson & Oliver Linton & Keith Vorkink, 2001. "Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach," Cahiers de recherche CREFE / CREFE Working Papers 143, CREFE, Université du Québec à Montréal.
    33. Hongjie Wei & Yan Sun, 2017. "Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(1), pages 113-128, January.
    34. Peter C.B. Phillips & Binbin Guo & Zhijie Xiao, 2002. "Efficient Regression in Time Series Partial Linear Models," Cowles Foundation Discussion Papers 1363, Cowles Foundation for Research in Economics, Yale University.
    35. Matias Cattaneo & Michael Jansson & Whitney K. Newey, 2015. "Alternative asymptotics and the partially linear model with many regressors," CeMMAP working papers 36/15, Institute for Fiscal Studies.
    36. Haotian Chen & Xibin Zhang, 2014. "Bayesian Estimation for Partially Linear Models with an Application to Household Gasoline Consumption," Monash Econometrics and Business Statistics Working Papers 28/14, Monash University, Department of Econometrics and Business Statistics.
    37. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    38. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.
    39. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    40. Xiao, Zhijie & Phillips, Peter C. B., 2002. "Higher order approximations for Wald statistics in time series regressions with integrated processes," Journal of Econometrics, Elsevier, vol. 108(1), pages 157-198, May.
    41. Chen, Songnian & Zhang, Hanghui, 2015. "Binary quantile regression with local polynomial smoothing," Journal of Econometrics, Elsevier, vol. 189(1), pages 24-40.
    42. Juhl, Ted & Xiao, Zhijie, 2005. "Testing for cointegration using partially linear models," Journal of Econometrics, Elsevier, vol. 124(2), pages 363-394, February.
    43. Liu, Ruixuan & Yu, Zhengfei, 2022. "Sample selection models with monotone control functions," Journal of Econometrics, Elsevier, vol. 226(2), pages 321-342.
    44. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
    45. Linton, Oliver, 2000. "Edgeworth approximations for semiparametric instrumental variable estimators and test statistics," LSE Research Online Documents on Economics 2156, London School of Economics and Political Science, LSE Library.
    46. Nielsen, Jens P. & Linton, Oliver & Bickel, Peter J., 1998. "On a semiparametric survival model with flexible covariate effect," LSE Research Online Documents on Economics 301, London School of Economics and Political Science, LSE Library.
    47. Fernanda Peixe & Alastair Hall & Kostas Kyriakoulis, 2006. "The Mean Squared Error of the Instrumental Variables Estimator When the Disturbance Has an Elliptical Distribution," Econometric Reviews, Taylor & Francis Journals, vol. 25(1), pages 117-138.
    48. Zhengyu Zhang, 2013. "A Pairwise Difference Estimator for Partially Linear Spatial Autoregressive Models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(2), pages 176-194, June.
    49. Xiao, Zhijie & Phillips, Peter C. B., 1998. "Higher-order approximations for frequency domain time series regression," Journal of Econometrics, Elsevier, vol. 86(2), pages 297-336, June.
    50. Sun, Yan, 2017. "Estimation of single-index model with spatial interaction," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 36-45.
    51. Fan, Yanqin & Ullah, Aman, 1999. "Asymptotic Normality of a Combined Regression Estimator," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 191-240, November.
    52. Oliver Linton, 2001. "Symmetrizing and unitizing transformations for linear smoother weights," Computational Statistics, Springer, vol. 16(1), pages 153-164, March.
    53. M. Dolores de Prada & Luis M. Borge, 1997. "Some methods for comparing first-order asymptotically equivalent estimators," Investigaciones Economicas, Fundación SEPI, vol. 21(3), pages 473-500, September.
    54. Wolfgang Härdle & Oliver Linton & Wang & Qihua, 2003. "Semiparametric regression analysis with missing response at random," CeMMAP working papers 11/03, Institute for Fiscal Studies.
    55. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    56. Yanqin Fan & Oliver Linton, 1997. "Some Higher Order Theory for a Consistent Nonparametric Model Specification Test," Cowles Foundation Discussion Papers 1148, Cowles Foundation for Research in Economics, Yale University.
    57. Byunghoon Kang, 2019. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Papers 1909.12162, arXiv.org, revised Feb 2020.
    58. Chaohua Dong & Jiti Gao & Oliver Linton, 2022. "Chaohua Dong, Jiti Gao and Oliver Linton’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 707-708, July.
    59. Oliver Linton, 1997. "Second Order Approximation in a Linear Regression with Heteroskedasticity for Unknown Form," Cowles Foundation Discussion Papers 1151, Cowles Foundation for Research in Economics, Yale University.
    60. Wu, Guojun & Xiao, Zhijie, 2002. "A generalized partially linear model of asymmetric volatility," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 287-319, August.
    61. Boente, Graciela & Vahnovan, Alejandra, 2017. "Robust estimators in semi-functional partial linear regression models," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 59-84.
    62. Tamaki, Kenichiro, 2007. "Second order optimality for estimators in time series regression models," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 638-659, March.
    63. Noh, Hohsuk & El Ghouch, Anouar & Van Keilegom, Ingrid, 2011. "Quality of fit measures in the framework of quantile regression," LIDAM Discussion Papers ISBA 2011025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    64. Germán Aneiros-Pérez & Philippe Vieu, 2011. "Automatic estimation procedure in partial linear model with functional data," Statistical Papers, Springer, vol. 52(4), pages 751-771, November.
    65. Dennis Kristensen, 2009. "Semiparametric Modelling and Estimation: A Selective Overview," CREATES Research Papers 2009-44, Department of Economics and Business Economics, Aarhus University.
    66. Su, Liangjun & Jin, Sainan, 2010. "Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 157(1), pages 18-33, July.
    67. Aneiros-Pérez, Germán, 2002. "On bandwidth selection in partial linear regression models under dependence," Statistics & Probability Letters, Elsevier, vol. 57(4), pages 393-401, May.

  140. Oliver Linton, 1993. "Adaptive Estimation in ARCH Models," Cowles Foundation Discussion Papers 1054, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Linton, Oliver, 1996. "Edgeworth Approximation for MINPIN Estimators in Semiparametric Regression Models," Econometric Theory, Cambridge University Press, vol. 12(1), pages 30-60, March.
    2. Thanasis Stengos & Ximing Wu, 2005. "Partially Adaptive Estimation via Maximum Entropy Densities," University of Cyprus Working Papers in Economics 6-2005, University of Cyprus Department of Economics.
    3. Sentana, Enrique & Fiorentini, Gabriele, 2018. "Specification tests for non-Gaussian maximum likelihood estimators," CEPR Discussion Papers 12934, C.E.P.R. Discussion Papers.
    4. Enno Mammen & Oliver Linton, 2004. "Estimating Semiparametric ARCH Models by Kernel Smoothing Methods," FMG Discussion Papers dp511, Financial Markets Group.
    5. Andreou, Elena & Werker, Bas J.M., 2015. "Residual-based rank specification tests for AR–GARCH type models," Journal of Econometrics, Elsevier, vol. 185(2), pages 305-331.
    6. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2021. "Bayesian estimation for a semiparametric nonlinear volatility model," Economic Modelling, Elsevier, vol. 98(C), pages 361-370.
    7. Iglesias, Emma M. & Linton, Oliver, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," UC3M Working papers. Economics we094726, Universidad Carlos III de Madrid. Departamento de Economía.
    8. W. K. Li & Shiqing Ling & Michael McAleer, 2001. "A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors," ISER Discussion Paper 0545, Institute of Social and Economic Research, Osaka University.
    9. Drost, F.C. & Klaasens, C.A.J. & Werker, B.J.M., 1994. "Adaptive Estimation in Time Series Models," Papers 9488, Tilburg - Center for Economic Research.
    10. Andreou, E. & Werker, B.J.M., 2004. "An Alternative Asymptotic Analysis of Residual-Based Statistics," Discussion Paper 2004-56, Tilburg University, Center for Economic Research.
    11. Oliver Linton & Dajing Shang & Yang Yan, 2012. "Efficient estimation of conditional risk measures in a semiparametric GARCH model," CeMMAP working papers 25/12, Institute for Fiscal Studies.
    12. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    13. Amengual, Dante & Fiorentini, Gabriele & Sentana, Enrique, 2013. "Sequential estimation of shape parameters in multivariate dynamic models," Journal of Econometrics, Elsevier, vol. 177(2), pages 233-249.
    14. HAFNER, Christian & ROMBOUTS, Jeroen, 2003. "Semiparametric multivariate GARCH models," LIDAM Discussion Papers CORE 2003003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Elena Andreou & Bas J.M. Werker, 2014. "Residual-based Rank Specification Tests for AR-GARCH type models," University of Cyprus Working Papers in Economics 02-2014, University of Cyprus Department of Economics.
    16. Gabriele Fiorentini & Enrique Sentana, 2019. "New testing approaches for mean-variance predictability," Working Paper series 19-01, Rimini Centre for Economic Analysis.
    17. Drost, Feike C. & Klaassen, Chris A. J., 1997. "Efficient estimation in semiparametric GARCH models," Journal of Econometrics, Elsevier, vol. 81(1), pages 193-221, November.
    18. Thanasis Stengos & Yiguo Sun, 2005. "The Absolute Health Income Hypothesis Revisited : A Semiparametric Quantile Regression Approach," University of Cyprus Working Papers in Economics 7-2005, University of Cyprus Department of Economics.
    19. Drost, F.C. & Werker, B.J.M., 1996. "Closing the GARCH gap : Continuous time GARCH modeling," Other publications TiSEM c3d29817-403a-4ad1-9295-8, Tilburg University, School of Economics and Management.
    20. Christian Francq & Jean-Michel Zakoïan, 2008. "A Tour in the Asymptotic Theory of GARCH Estimation," Working Papers 2008-03, Center for Research in Economics and Statistics.
    21. Xibin Zhang & Maxwell L. King, 2011. "Bayesian semiparametric GARCH models," Monash Econometrics and Business Statistics Working Papers 24/11, Monash University, Department of Econometrics and Business Statistics.
    22. Francq, Christian & Zakoian, Jean-Michel, 2021. "Local asymptotic normality of general conditionally heteroskedastic and score-driven time-series models," MPRA Paper 106542, University Library of Munich, Germany.
    23. Calzolari, Giorgio & Fiorentini, Gabriele, 1994. "Conditional heteroskedasticity in nonlinear simultaneous equations," MPRA Paper 24428, University Library of Munich, Germany.
    24. Peter Hall, 2007. "Comments on: Nonparametric inference with generalized likelihood ratio tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(3), pages 448-449, December.
    25. Hodgson, Douglas J & Vorkink, Keith P, 2003. "Efficient Estimation of Conditional Asset-Pricing Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 269-283, April.
    26. Drost, F.C. & Werker, B.J.M., 2001. "Semiparametric Duration Models," Other publications TiSEM 845b71c6-9525-4006-a0df-4, Tilburg University, School of Economics and Management.
    27. Oliver Linton & Douglas G. Steigerwald, 1995. "Adaptive Testing in ARCH Models," Cowles Foundation Discussion Papers 1105, Cowles Foundation for Research in Economics, Yale University.
    28. MEDDAHI, Nour & RENAULT, Éric, 1998. "Quadratic M-Estimators for ARCH-Type Processes," Cahiers de recherche 9814, Universite de Montreal, Departement de sciences economiques.
    29. Andreou, E. & Werker, B.J.M., 2004. "An Alternative Asymptotic Analysis of Residual-Based Statistics," Other publications TiSEM 93fe16c1-9f21-4dab-9b73-4, Tilburg University, School of Economics and Management.
    30. Xibin Zhang & Maxwell L. King, 2013. "Gaussian kernel GARCH models," Monash Econometrics and Business Statistics Working Papers 19/13, Monash University, Department of Econometrics and Business Statistics.
    31. Rombouts, Jeroen V. K. & Hafner, Christian M., 2004. "Semiparametric multivariate volatility models," Papers 2004,14, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    32. Hallin, Marc & La Vecchia, Davide, 2017. "R-estimation in semiparametric dynamic location-scale models," Journal of Econometrics, Elsevier, vol. 196(2), pages 233-247.
    33. Mimoto, Nao, 2008. "Convergence in distribution for the sup-norm of a kernel density estimator for GARCH innovations," Statistics & Probability Letters, Elsevier, vol. 78(7), pages 915-923, May.
    34. Douglas J. Hodgson & Oliver Linton & Keith Vorkink, 2001. "Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach," Cahiers de recherche CREFE / CREFE Working Papers 143, CREFE, Université du Québec à Montréal.
    35. Eduardo Rossi, 2010. "Univariate GARCH models: a survey (in Russian)," Quantile, Quantile, issue 8, pages 1-67, July.
    36. Hodgson, Douglas J., 1998. "Adaptive estimation of cointegrating regressions with ARMA errors," Journal of Econometrics, Elsevier, vol. 85(2), pages 231-267, August.
    37. Safarian, Mher, 2013. "On portfolio risk estimation," Working Paper Series in Economics 52, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    38. Drost, F.C. & Klaassen, C.A.J., 1996. "Efficient Estimation in Semiparametric GARCH Models," Other publications TiSEM 3da5ac9e-1f93-41b2-aaa0-5, Tilburg University, School of Economics and Management.
    39. Andreou, E. & Werker, B.J.M., 2003. "A Simple Asymptotic Analysis of Residual-Based Statistics," Other publications TiSEM 9fe68e51-a026-4660-b6e7-8, Tilburg University, School of Economics and Management.
    40. Oliver Linton & Dajing Shang & Yang Yan, 2012. "Efficient estimation of conditional risk measures in a semiparametric GARCH model," CeMMAP working papers CWP25/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    41. Linton, Oliver & Mammen, Enno, 2004. "Estimating semiparametric ARCH (∞) models by kernel smoothing methods," LSE Research Online Documents on Economics 24762, London School of Economics and Political Science, LSE Library.
    42. Bai, Jushan & Ng, Serena, 2001. "A consistent test for conditional symmetry in time series models," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 225-258, July.
    43. Gabriele Fiorentini & Enrique Sentana, 2007. "On the efficiency and consistency of likelihood estimation in multivariate conditionally heteroskedastic dynamic regression models," Working Paper series 38_07, Rimini Centre for Economic Analysis.
    44. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    45. O. Linton & E. Mammen, 2005. "Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods," Econometrica, Econometric Society, vol. 73(3), pages 771-836, May.
    46. Douglas Hodgson, 2002. "Semiparametric Efficient Estimation of the Mean of a Time Series in the Presence of Conditional Heterogeneity of Unknown Form," Cahiers de recherche CREFE / CREFE Working Papers 146, CREFE, Université du Québec à Montréal.
    47. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    48. Gonzalez-Rivera, G. & Drost, F.C., 1998. "Efficiency comparisons of maximum likelihood-based estimators in garch models," Discussion Paper 1998-124, Tilburg University, Center for Economic Research.
    49. Jianqing Fan & Lei Qi & Dacheng Xiu, 2014. "Quasi-Maximum Likelihood Estimation of GARCH Models With Heavy-Tailed Likelihoods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 178-191, April.
    50. Douglas Hodgson & Barrett Slade & Keith Vorkink, 2006. "Constructing Commercial Indices: A Semiparametric Adaptive Estimator Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 32(2), pages 151-168, March.
    51. Werker, Bas J M & Andreou, Elena, 2013. "Residual-based Rank Specification Tests for AR-GARCH type models," CEPR Discussion Papers 9583, C.E.P.R. Discussion Papers.
    52. Shiqing Ling & Michael McAleer, 2001. "On Adaptive Estimation in Nonstationary ARMA Models with GARCH Errors," ISER Discussion Paper 0548, Institute of Social and Economic Research, Osaka University.
    53. Chen, Songnian & Zhou, Yahong, 2010. "Semiparametric and nonparametric estimation of sample selection models under symmetry," Journal of Econometrics, Elsevier, vol. 157(1), pages 143-150, July.
    54. Amano, Tomoyuki & Taniguchi, Masanobu, 2008. "Asymptotic efficiency of conditional least squares estimators for ARCH models," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 179-185, February.
    55. Marc Hallin & Davide La Vecchia, 2014. "Semiparametrically Efficient R-Estimation for Dynamic Location-Scale Models," Working Papers ECARES ECARES 2014-45, ULB -- Universite Libre de Bruxelles.
    56. Ciccarelli Nicola, 2018. "Semiparametric efficient adaptive estimation of the GJR-GARCH model," Statistics & Risk Modeling, De Gruyter, vol. 35(3-4), pages 141-160, July.
    57. Xuejie Feng & Chiping Zhang, 2020. "A Perturbation Method to Optimize the Parameters of Autoregressive Conditional Heteroscedasticity Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 1021-1044, March.
    58. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    59. Chen, Songnian, 2000. "Rank estimation of a location parameter in the binary choice model," Journal of Econometrics, Elsevier, vol. 98(2), pages 317-334, October.
    60. Andreou, E. & Werker, B.J.M., 2003. "A Simple Asymptotic Analysis of Residual-Based Statistics," Discussion Paper 2003-118, Tilburg University, Center for Economic Research.

  141. Oliver LINTON, "undated". "Kernel estimation in a nonparametric marker dependent Hazard Model," Statistic und Oekonometrie 9313, Humboldt Universitaet Berlin.

    Cited by:

    1. Ingrid Van Keilegom & Noël Veraverbeke, 2001. "Hazard Rate Estimation in Nonparametric Regression with Censored Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(4), pages 730-745, December.
    2. Toshio Honda, 2005. "Estimation in additive cox models by marginal integration," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 403-423, September.
    3. Gorgens, T., 1999. "Semiparametric Estimation of Single-Index Transition Intensities," Papers 99-25, Carleton - School of Public Administration.
    4. Perch Nielsen, Jens, 2000. "Super-Efficient Prediction Based on High-Quality Marker Information," Finance Working Papers 00-5, University of Aarhus, Aarhus School of Business, Department of Business Studies.

Articles

  1. Z. Merrick Li & Oliver Linton, 2022. "A ReMeDI for Microstructure Noise," Econometrica, Econometric Society, vol. 90(1), pages 367-389, January.
    See citations under working paper version above.
  2. Ai, Chunrong & Linton, Oliver & Zhang, Zheng, 2022. "Estimation and inference for the counterfactual distribution and quantile functions in continuous treatment models," Journal of Econometrics, Elsevier, vol. 228(1), pages 39-61.

    Cited by:

    1. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    2. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    3. Yu-Chin Hsu & Martin Huber & Yu-Min Yen, 2023. "Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning," Papers 2307.01049, arXiv.org.

  3. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    See citations under working paper version above.
  4. Oliver Linton & Yoon Jae Whang & Yu-Min Yen, 2021. "The lower regression function and testing expectation dependence dominance hypotheses," Econometric Reviews, Taylor & Francis Journals, vol. 40(8), pages 709-727, September.
    See citations under working paper version above.
  5. Ma, Shujie & Linton, Oliver & Gao, Jiti, 2021. "Estimation and inference in semiparametric quantile factor models," Journal of Econometrics, Elsevier, vol. 222(1), pages 295-323.
    See citations under working paper version above.
  6. Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.

    Cited by:

    1. Tingting Cheng & Chaohua Dong & Jiti Gao & Oliver Linton, 2022. "GMM Estimation for High-Dimensional Panel Data Models," Monash Econometrics and Business Statistics Working Papers 11/22, Monash University, Department of Econometrics and Business Statistics.
    2. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    3. Dong, C. & Li, S., 2021. "Specification Lasso and an Application in Financial Markets," Cambridge Working Papers in Economics 2139, Faculty of Economics, University of Cambridge.
    4. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP69/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
    6. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2021. "Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice," Monash Econometrics and Business Statistics Working Papers 18/21, Monash University, Department of Econometrics and Business Statistics.
    7. Peng, Zhen & Dong, Chaohua, 2022. "Augmented cointegrating linear models with possibly strongly correlated stationary and nonstationary regressors," Finance Research Letters, Elsevier, vol. 47(PB).
    8. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2021. "Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice," Papers 2111.02023, arXiv.org.

  7. Escanciano, Juan Carlos & Hoderlein, Stefan & Lewbel, Arthur & Linton, Oliver & Srisuma, Sorawoot, 2021. "Nonparametric Euler Equation Identification And Estimation," Econometric Theory, Cambridge University Press, vol. 37(5), pages 851-891, October.
    See citations under working paper version above.
  8. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    See citations under working paper version above.
  9. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.
    See citations under working paper version above.
  10. Hafner, Christian M. & Linton, Oliver B. & Tang, Haihan, 2020. "Estimation of a multiplicative correlation structure in the large dimensional case," Journal of Econometrics, Elsevier, vol. 217(2), pages 431-470.
    See citations under working paper version above.
  11. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    See citations under working paper version above.
  12. Hong, Seok Young & Linton, Oliver, 2020. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Journal of Econometrics, Elsevier, vol. 219(2), pages 389-424.
    See citations under working paper version above.
  13. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.

    Cited by:

    1. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    2. Insana, Alessandra, 2023. "Betting against beta with intraday and overnight signals," International Review of Financial Analysis, Elsevier, vol. 86(C).
    3. Giuseppe Buccheri & Stefano Grassi & Giorgio Vocalelli, 2021. "Estimating Risk in Illiquid Markets: a Model of Market Friction with Stochastic Volatility," CEIS Research Paper 506, Tor Vergata University, CEIS, revised 08 Nov 2021.
    4. Alanya-Beltran Willy, 2023. "Modelling volatility dependence with score copula models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(5), pages 649-668, December.
    5. Tianyi Wang & Sicong Cheng & Fangsheng Yin & Mei Yu, 2022. "Overnight volatility, realized volatility, and option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1264-1283, July.
    6. Enzo D'Innocenzo & Alessandra Luati & Mario Mazzocchi, 2020. "A Robust Score-Driven Filter for Multivariate Time Series," Papers 2009.01517, arXiv.org, revised Aug 2022.
    7. Opschoor, Anne & Lucas, André, 2021. "Observation-driven models for realized variances and overnight returns applied to Value-at-Risk and Expected Shortfall forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 622-633.
    8. Insana, Alessandra, 2022. "Does systematic risk change when markets close? An analysis using stocks’ beta," Economic Modelling, Elsevier, vol. 109(C).

  14. Gao, Jiti & Linton, Oliver & Peng, Bin, 2020. "Inference On A Semiparametric Model With Global Power Law And Local Nonparametric Trends," Econometric Theory, Cambridge University Press, vol. 36(2), pages 223-249, April.
    See citations under working paper version above.
  15. Ba M. Chu & David T. Jacho-Chávez & Oliver B. Linton, 2020. "Standard Errors for Nonparametric Regression," Econometric Reviews, Taylor & Francis Journals, vol. 39(7), pages 674-690, August.

    Cited by:

    1. Zhang, Anan & Zheng, Yadi & Huang, Huang & Ding, Ning & Zhang, Chengqian, 2022. "Co-integration theory-based cluster time-varying load optimization control model of regional integrated energy system," Energy, Elsevier, vol. 260(C).

  16. Linton, Oliver & Xiao, Zhijie, 2019. "Efficient estimation of nonparametric regression in the presence of dynamic heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(2), pages 608-631.
    See citations under working paper version above.
  17. Chen, Jia & Li, Degui & Linton, Oliver, 2019. "A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables," Journal of Econometrics, Elsevier, vol. 212(1), pages 155-176.
    See citations under working paper version above.
  18. Auld, Tom & Linton, Oliver, 2019. "The behaviour of betting and currency markets on the night of the EU referendum," International Journal of Forecasting, Elsevier, vol. 35(1), pages 371-389.
    See citations under working paper version above.
  19. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2018. "Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 919-932, April.

    Cited by:

    1. Jingwen Tu & Hu Yang & Chaohui Guo & Jing Lv, 2021. "Model averaging marginal regression for high dimensional conditional quantile prediction," Statistical Papers, Springer, vol. 62(6), pages 2661-2689, December.
    2. Hong, Seok Young & Linton, Oliver, 2020. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Journal of Econometrics, Elsevier, vol. 219(2), pages 389-424.
    3. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    4. Wei, Jie & Chen, Hui, 2020. "Determining the number of factors in approximate factor models by twice K-fold cross validation," Economics Letters, Elsevier, vol. 191(C).
    5. Daouia, Abdelaati & Stupfler, Gilles & Usseglio-Carleve, Antoine, 2022. "Inference for extremal regression with dependent heavy-tailed data," TSE Working Papers 22-1324, Toulouse School of Economics (TSE), revised 29 Aug 2023.
    6. Yongmiao Hong & Tae-Hwy Lee & Yuying Sun & Shouyang Wang & Xinyu Zhang, 2017. "Time-varying Model Averaging," Working Papers 202001, University of California at Riverside, Department of Economics.
    7. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
    8. Lin, Boqiang & Xu, Bin, 2019. "How to effectively stabilize China's commodity price fluctuations?," Energy Economics, Elsevier, vol. 84(C).
    9. Chen, Jia & Li, Degui & Linton, Oliver, 2019. "A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables," Journal of Econometrics, Elsevier, vol. 212(1), pages 155-176.
    10. Liao, Jun & Wan, Alan T.K. & He, Shuyuan & Zou, Guohua, 2022. "Optimal model averaging for multivariate regression models," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    11. Yuan, Chaoxia & Fang, Fang & Ni, Lyu, 2022. "Mallows model averaging with effective model size in fragmentary data prediction," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    12. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, vol. 7(3), pages 1-26, September.
    13. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202302, University of Kansas, Department of Economics, revised Jan 2023.
    14. Liao, Jun & Zou, Guohua, 2020. "Corrected Mallows criterion for model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    15. Liao, Jun & Zong, Xianpeng & Zhang, Xinyu & Zou, Guohua, 2019. "Model averaging based on leave-subject-out cross-validation for vector autoregressions," Journal of Econometrics, Elsevier, vol. 209(1), pages 35-60.
    16. Fang, Fang & Li, Jialiang & Xia, Xiaochao, 2022. "Semiparametric model averaging prediction for dichotomous response," Journal of Econometrics, Elsevier, vol. 229(2), pages 219-245.
    17. Yuying Sun & Shaoxin Hong & Zongwu Cai, 2023. "Optimal Local Model Averaging for Divergent-Dimensional Functional-Coefficient Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202309, University of Kansas, Department of Economics, revised Sep 2023.
    18. Guo, Chaohui & Lv, Jing & Wu, Jibo, 2021. "Composite quantile regression for ultra-high dimensional semiparametric model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    19. De Gooijer Jan G. & Zerom Dawit, 2020. "Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-15, January.

  20. Oliver Linton & Soheil Mahmoodzadeh, 2018. "Implications of High-Frequency Trading for Security Markets," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 237-259, August.
    See citations under working paper version above.
  21. Dong, Chaohua & Linton, Oliver, 2018. "Additive nonparametric models with time variable and both stationary and nonstationary regressors," Journal of Econometrics, Elsevier, vol. 207(1), pages 212-236.
    See citations under working paper version above.
  22. Chen, Xiaohong & Linton, Oliver & Yi, Yanping, 2017. "Semiparametric identification of the bid–ask spread in extended Roll models," Journal of Econometrics, Elsevier, vol. 200(2), pages 312-325.

    Cited by:

    1. Chen, Xiaohong & Linton, Oliver & Schneeberger, Stefan & Yi, Yanping, 2019. "Semiparametric estimation of the bid–ask spread in extended roll models," Journal of Econometrics, Elsevier, vol. 208(1), pages 160-178.
    2. Duda Jarosław & Gurgul Henryk & Syrek Robert, 2020. "Modelling bid-ask spread conditional distributions using hierarchical correlation reconstruction," Statistics in Transition New Series, Polish Statistical Association, vol. 21(5), pages 99-118, December.

  23. Hafner, Christian M. & Linton, Oliver, 2017. "An Almost Closed Form Estimator For The Egarch Model," Econometric Theory, Cambridge University Press, vol. 33(4), pages 1013-1038, August.
    See citations under working paper version above.
  24. Gordon Anderson & Oliver Linton & Jasmin Thomas, 2017. "Similarity, dissimilarity and exceptionality: generalizing Gini’s transvariation to measure “differentness” in many distributions," METRON, Springer;Sapienza Università di Roma, vol. 75(2), pages 161-180, August.

    Cited by:

    1. Anderson, G. & Linton, O. & Pittau, M G. & Whang, Y-J. & Zelli, R., 2020. "On Unit Free Assessment of The Extent of Multilateral Distributional Variation," Cambridge Working Papers in Economics 20123, Faculty of Economics, University of Cambridge.
    2. Gordon Anderson, Alessio Farcomeni, Maria Grazia Pittau and Roberto Zelli, 2019. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development Since 1990," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(1), pages 1-22, March.
    3. Gordon Anderson & Jasmin Thomas, 2019. "Measuring Multi-group Polarization, Segmentation and Ambiguity: Increasingly Unequal Yet Similar Constituent Canadian Income Distributions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(3), pages 1001-1032, October.
    4. Gordon Anderson & Maria Grazia Pittau & Roberto Zelli & Jasmin Thomas, 2018. "Income Inequality, Cohesiveness and Commonality in the Euro Area: A Semi-Parametric Boundary-Free Analysis," Econometrics, MDPI, vol. 6(2), pages 1-20, March.
    5. Gordon Anderson & Tongtong Hao & Maria Grazia Pittau, 2019. "More unequal yet more alike, the changing patterns of family formation, generational mobility and household income inequality in China: a counter-factual analysis," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(3), pages 359-378, September.
    6. Gordon Anderson, 2018. "Measuring Aspects of Mobility, Polarization and Convergence in the Absence of Cardinality: Indices Based Upon Transitional Typology," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(3), pages 887-907, October.

  25. Michael Vogt & Oliver Linton, 2017. "Classification of non-parametric regression functions in longitudinal data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 5-27, January.

    Cited by:

    1. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2023. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Papers 2303.13218, arXiv.org.
    2. Miao, Ke & Su, Liangjun & Wang, Wendun, 2020. "Panel threshold regressions with latent group structures," Journal of Econometrics, Elsevier, vol. 214(2), pages 451-481.
    3. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    4. Wang, Wei & Xiao, Zhijie & Ren, Yanyan & Yan, Xiaodong, 2023. "A bi-integrative analysis of two-dimensional heterogeneous panel data models," Economics Letters, Elsevier, vol. 230(C).
    5. Liebl, Dominik & Walders, Fabian, 2019. "Parameter regimes in partial functional panel regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 105-115.
    6. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Inference of Grouped Time-Varying Network Vector Autoregression Models," Monash Econometrics and Business Statistics Working Papers 5/23, Monash University, Department of Econometrics and Business Statistics.
    7. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.
    8. Jia Chen, 2018. "Estimating Latent Group Structure in Time-Varying Coefficient Panel Data Models," Discussion Papers 18/15, Department of Economics, University of York.
    9. Xi Chen & Ye Luo & Martin Spindler, 2019. "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers 1912.12867, arXiv.org, revised Jan 2020.
    10. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    11. Gao, Jiti & Xia, Kai & Zhu, Huanjun, 2020. "Heterogeneous panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 219(2), pages 329-353.
    12. Su, Liangjun & Wang, Wuyi & Xu, Xingbai, 2023. "Identifying latent group structures in spatial dynamic panels," Journal of Econometrics, Elsevier, vol. 235(2), pages 1955-1980.
    13. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Estimation of Grouped Time-Varying Network Vector Autoregression Models," Papers 2303.10117, arXiv.org, revised Mar 2024.

  26. Lena Boneva & Oliver Linton, 2017. "A discrete†choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1226-1243, November.

    Cited by:

    1. Ando, Tomohiro & Bai, Jushan & Li, Kunpeng, 2022. "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 230(1), pages 20-38.
    2. Ando, Tomohiro & Bai, Jushan, 2021. "Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity," MPRA Paper 111431, University Library of Munich, Germany.
    3. Williams, Benjamin, 2020. "Nonparametric identification of discrete choice models with lagged dependent variables," Journal of Econometrics, Elsevier, vol. 215(1), pages 286-304.
    4. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Papers 2012.03182, arXiv.org, revised Nov 2021.
    5. Artūras Juodis, 2022. "A regularization approach to common correlated effects estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 788-810, June.
    6. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2021. "MCMC Conditional Maximum Likelihood for the two-way fixed-effects logit," MPRA Paper 110034, University Library of Munich, Germany.
    7. Andrea Zaghini, 2017. "The CSPP at work: yield heterogeneity and the portfolio rebalancing channel," Temi di discussione (Economic working papers) 1157, Bank of Italy, Economic Research and International Relations Area.
    8. Mingli Chen & Iv'an Fern'andez-Val & Martin Weidner, 2014. "Nonlinear Factor Models for Network and Panel Data," Papers 1412.5647, arXiv.org, revised Oct 2019.
    9. Jiti Gao & Fei Liu & Bin peng, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 44/20, Monash University, Department of Econometrics and Business Statistics.
    10. Ye, Xiaoqing & Xu, Juan & Wu, Xiangjun, 2018. "Estimation of an unbalanced panel data Tobit model with interactive effects," Journal of choice modelling, Elsevier, vol. 28(C), pages 108-123.
    11. Eberhardt, Markus, 2018. "(At Least) Four Theories for Sovereign Default," CEPR Discussion Papers 13084, C.E.P.R. Discussion Papers.
    12. Nicola Borri & Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2024. "One Factor to Bind the Cross-Section of Returns," NBER Working Papers 32365, National Bureau of Economic Research, Inc.
    13. Mugnier, Martin & Wang, Ao, 2022. "Identification and (Fast) Estimation of Large Nonlinear Panel Models with Two-Way Fixed Effects," The Warwick Economics Research Paper Series (TWERPS) 1422, University of Warwick, Department of Economics.
    14. Mr. Markus Eberhardt & Mr. Andrea F Presbitero, 2018. "Commodity Price Movements and Banking Crises," IMF Working Papers 2018/153, International Monetary Fund.
    15. Arturas Juodis & Simon Reese, 2018. "The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation," Papers 1810.03715, arXiv.org, revised Feb 2021.
    16. Rachel Cho & Rodolphe Desbordes & Markus Eberhardt, 2022. "The causal effects of the darker side of financial development," Discussion Papers 2022-04, University of Nottingham, GEP.
    17. Liang Chen & Minyuan Zhang, 2023. "Common Correlated Effects Estimation of Nonlinear Panel Data Models," Papers 2304.13199, arXiv.org.
    18. Jiti Gao & Oliver Linton & Bin Peng, 2022. "A Nonparametric Panel Model for Climate Data with Seasonal and Spatial Variation," Monash Econometrics and Business Statistics Working Papers 9/22, Monash University, Department of Econometrics and Business Statistics.
    19. Jie Wei & Yonghui Zhang, 2022. "Panel Probit Models with Time‐Varying Individual Effects: Reestimating the Effects of Fertility on Female Labour Participation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 799-829, August.
    20. Feng, Guohua & Peng, Bin & Su, Liangjun & Yang, Thomas Tao, 2019. "Semi-parametric single-index panel data models with interactive fixed effects: Theory and practice," Journal of Econometrics, Elsevier, vol. 212(2), pages 607-622.
    21. Rodolphe Desbordes & Markus Eberhardt, 2019. "Gravity," Discussion Papers 2019-02, University of Nottingham, GEP.
    22. Lee, Yoonseok & Sul, Donggyu, 2023. "Depth-weighted means of noisy data: An application to estimating the average effect in heterogeneous panels," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    23. Feng, Qu, 2020. "Common factors and common breaks in panels: An empirical investigation," Economics Letters, Elsevier, vol. 187(C).
    24. Chen, Jia & Shin, Yongcheol & Zheng, Chaowen, 2022. "Estimation and inference in heterogeneous spatial panels with a multifactor error structure," Journal of Econometrics, Elsevier, vol. 229(1), pages 55-79.

  27. Chen, Xiaohong & Jacho-Chávez, David T. & Linton, Oliver, 2016. "Averaging Of An Increasing Number Of Moment Condition Estimators," Econometric Theory, Cambridge University Press, vol. 32(1), pages 30-70, February.

    Cited by:

    1. S. Boragan Aruoba & Ronel Elul & Sebnem Kalemli Ozcan, 2022. "Housing Wealth and Consumption: The Role of Heterogeneous Credit Constraints," Working Papers 22-34, Federal Reserve Bank of Philadelphia.
    2. Lena Boneva (Körber) & Oliver Linton, 2017. "A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance," CeMMAP working papers CWP02/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Chaudhuri, Saraswata & Renault, Eric, 2023. "Efficient estimation of regression models with user-specified parametric model for heteroskedasticty," The Warwick Economics Research Paper Series (TWERPS) 1473, University of Warwick, Department of Economics.
    4. Frank Windmeijer, 2017. "Two-Stage Least Squares as Minimum Distance," Bristol Economics Discussion Papers 17/683, School of Economics, University of Bristol, UK, revised 13 Jun 2018.
    5. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
    6. Yoonseok Lee & Yu Zhou, 2015. "Averaged Instrumental Variables Estimators," Center for Policy Research Working Papers 180, Center for Policy Research, Maxwell School, Syracuse University.
    7. Cizek, P. & Lei, J., 2013. "Identification and Estimation of Nonseparable Single-Index Models in Panel Data with Correlated Random Effects," Other publications TiSEM 73e394eb-6799-4c79-af23-8, Tilburg University, School of Economics and Management.
    8. Matthew Harding & Carlos Lamarche & Chris Muris, 2022. "Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data," Papers 2203.03051, arXiv.org.
    9. Mengli Zhang & Yang Bai, 2021. "On the use of repeated measurement errors in linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 779-803, July.
    10. Zhu, Qianqian & Zheng, Yao & Li, Guodong, 2018. "Linear double autoregression," Journal of Econometrics, Elsevier, vol. 207(1), pages 162-174.

  28. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    See citations under working paper version above.
  29. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    See citations under working paper version above.
  30. Chen, Jia & Li, Degui & Linton, Oliver & Lu, Zudi, 2016. "Semiparametric dynamic portfolio choice with multiple conditioning variables," Journal of Econometrics, Elsevier, vol. 194(2), pages 309-318.
    See citations under working paper version above.
  31. Park, Sujin & Hong, Seok Young & Linton, Oliver, 2016. "Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error," Journal of Econometrics, Elsevier, vol. 191(2), pages 325-347.

    Cited by:

    1. Maria Elvira Mancino & Tommaso Mariotti & Giacomo Toscano, 2022. "Asymptotic Normality for the Fourier spot volatility estimator in the presence of microstructure noise," Papers 2209.08967, arXiv.org.
    2. Markus Bibinger & Per A. Mykland, 2016. "Inference for Multi-dimensional High-frequency Data with an Application to Conditional Independence Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1078-1102, December.
    3. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    4. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Janeway Institute Working Papers 2208, Faculty of Economics, University of Cambridge.
    5. Mykland, Per A. & Zhang, Lan & Chen, Dachuan, 2019. "The algebra of two scales estimation, and the S-TSRV: High frequency estimation that is robust to sampling times," Journal of Econometrics, Elsevier, vol. 208(1), pages 101-119.
    6. Hafner, Christian & Linton, Oliver & Tang, Haihan, 2020. "Estimation of a multiplicative correlation structure in the large dimensional case," LIDAM Reprints ISBA 2020028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Fan, Jianqing & Kim, Donggyu, 2019. "Structured volatility matrix estimation for non-synchronized high-frequency financial data," Journal of Econometrics, Elsevier, vol. 209(1), pages 61-78.
    8. Richard Y. Chen, 2019. "The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations," Papers 1911.02205, arXiv.org.

  32. Linton, Oliver & Wang, Qiying, 2016. "Nonparametric Transformation Regression With Nonstationary Data," Econometric Theory, Cambridge University Press, vol. 32(1), pages 1-29, February.
    See citations under working paper version above.
  33. Linton, Oliver & Smetanina, Ekaterina, 2016. "Testing the martingale hypothesis for gross returns," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 664-689.

    Cited by:

    1. Andrey Shternshis & Piero Mazzarisi & Stefano Marmi, 2022. "Efficiency of the Moscow Stock Exchange before 2022," Papers 2207.10476, arXiv.org, revised Jul 2022.

  34. Lena Boneva & Oliver Linton & Michael Vogt, 2016. "The Effect of Fragmentation in Trading on Market Quality in the UK Equity Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 192-213, January.
    See citations under working paper version above.
  35. Koo, Bonsoo & Linton, Oliver, 2015. "Let’S Get Lade: Robust Estimation Of Semiparametric Multiplicative Volatility Models," Econometric Theory, Cambridge University Press, vol. 31(4), pages 671-702, August.
    See citations under working paper version above.
  36. Boneva, Lena & Linton, Oliver & Vogt, Michael, 2015. "A semiparametric model for heterogeneous panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 327-345.
    See citations under working paper version above.
  37. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.

    Cited by:

    1. Tu, Yundong & Yi, Yanping, 2017. "Forecasting cointegrated nonstationary time series with time-varying variance," Journal of Econometrics, Elsevier, vol. 196(1), pages 83-98.
    2. Jingwen Tu & Hu Yang & Chaohui Guo & Jing Lv, 2021. "Model averaging marginal regression for high dimensional conditional quantile prediction," Statistical Papers, Springer, vol. 62(6), pages 2661-2689, December.
    3. Michele Battisti & Massimo Del Gatto & Christopher F. Parmeter, 2018. "Labor productivity growth: disentangling technology and capital accumulation," Journal of Economic Growth, Springer, vol. 23(1), pages 111-143, March.
    4. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    5. Kotlyarova, Yulia & Schafgans, Marcia M.A. & Zinde-Walsh, Victoria, 2021. "Rates of expansions for functional estimators," LSE Research Online Documents on Economics 113436, London School of Economics and Political Science, LSE Library.
    6. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric Model Averaging of Ultra-High Dimensional Time Series," Discussion Papers 15/18, Department of Economics, University of York.
    7. Tommaso Proietti & Alessandro Giovannelli, 2020. "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper 482, Tor Vergata University, CEIS, revised 12 May 2020.
    8. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric dynamic portfolio choice with multiple conditioning variables," CeMMAP working papers CWP07/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Zhang, Xinyu & Ullah, Aman & Zhao, Shangwei, 2016. "On the dominance of Mallows model averaging estimator over ordinary least squares estimator," Economics Letters, Elsevier, vol. 142(C), pages 69-73.
    10. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
    11. Chen, Jia & Li, Degui & Linton, Oliver, 2019. "A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables," Journal of Econometrics, Elsevier, vol. 212(1), pages 155-176.
    12. Jialiang Li & Tonghui Yu & Jing Lv & Mei‐Ling Ting Lee, 2021. "Semiparametric model averaging prediction for lifetime data via hazards regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1187-1209, November.
    13. Zishu Zhan & Yang Li & Yuhong Yang & Cunjie Lin, 2023. "Model averaging for semiparametric varying coefficient quantile regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(4), pages 649-681, August.
    14. Yuan, Chaoxia & Fang, Fang & Ni, Lyu, 2022. "Mallows model averaging with effective model size in fragmentary data prediction," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    15. Aman Ullah & Xinyu Zhang, 2015. "Grouped Model Averaging for Finite Sample Size," Working Papers 201501, University of California at Riverside, Department of Economics.
    16. Tao Huang & Jialiang Li, 2018. "Semiparametric model average prediction in panel data analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 125-144, January.
    17. Fang, Fang & Li, Jialiang & Xia, Xiaochao, 2022. "Semiparametric model averaging prediction for dichotomous response," Journal of Econometrics, Elsevier, vol. 229(2), pages 219-245.
    18. Xiaochao Xia, 2021. "Model averaging prediction for nonparametric varying-coefficient models with B-spline smoothing," Statistical Papers, Springer, vol. 62(6), pages 2885-2905, December.
    19. Guo, Chaohui & Lv, Jing & Wu, Jibo, 2021. "Composite quantile regression for ultra-high dimensional semiparametric model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    20. Fang, Fang & Yu, Zhou, 2020. "Model averaging assisted sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).

  38. Oliver Linton & Thierry Post & Yoon‐Jae Whang, 2014. "Testing for the stochastic dominance efficiency of a given portfolio," Econometrics Journal, Royal Economic Society, vol. 17(2), pages 59-74, June.
    See citations under working paper version above.
  39. Michael Vogt & Oliver Linton, 2014. "Nonparametric estimation of a periodic sequence in the presence of a smooth trend," Biometrika, Biometrika Trust, vol. 101(1), pages 121-140.
    See citations under working paper version above.
  40. Battey, Heather & Linton, Oliver, 2014. "Nonparametric estimation of multivariate elliptic densities via finite mixture sieves," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 43-67.
    See citations under working paper version above.
  41. Oliver Linton & Pedro Gozalo, 2014. "Testing Conditional Independence Restrictions," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 523-552, August.

    Cited by:

    1. Yu-Chin Hsu & Ta-Cheng Huang & Haiqing Xu, 2018. "Testing for Unobserved Heterogeneous Treatment Effects with Observational Data," Papers 1803.07514, arXiv.org, revised Aug 2021.
    2. Su, Liangjun & Zheng, Xin, 2017. "A martingale-difference-divergence-based test for specification," Economics Letters, Elsevier, vol. 156(C), pages 162-167.
    3. Xiaojun Song & Haoyu Wei, 2021. "Nonparametric Tests of Conditional Independence for Time Series," Papers 2110.04847, arXiv.org.
    4. Xuehu Zhu & Jun Lu & Jun Zhang & Lixing Zhu, 2021. "Testing for conditional independence: A groupwise dimension reduction‐based adaptive‐to‐model approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 549-576, June.

  42. Linton, Oliver & Xiao, Zhijie, 2013. "Estimation Of And Inference About The Expected Shortfall For Time Series With Infinite Variance," Econometric Theory, Cambridge University Press, vol. 29(4), pages 771-807, August.

    Cited by:

    1. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
    2. Steven Kou & Xianhua Peng, 2014. "On the Measurement of Economic Tail Risk," Papers 1401.4787, arXiv.org, revised Aug 2015.
    3. Chen, An & Nguyen, Thai & Stadje, Mitja, 2018. "Optimal investment under VaR-Regulation and Minimum Insurance," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 194-209.
    4. Daouia, Abdelaati & Stupfler, Gilles & Usseglio-Carleve, Antoine, 2022. "Inference for extremal regression with dependent heavy-tailed data," TSE Working Papers 22-1324, Toulouse School of Economics (TSE), revised 29 Aug 2023.
    5. Hill, Jonathan B. & Prokhorov, Artem, 2016. "GEL estimation for heavy-tailed GARCH models with robust empirical likelihood inference," Journal of Econometrics, Elsevier, vol. 190(1), pages 18-45.
    6. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    7. Trapani, Lorenzo, 2016. "Testing for (in)finite moments," Journal of Econometrics, Elsevier, vol. 191(1), pages 57-68.
    8. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    9. Linton, O. & Whang, Y-J. & Yen, Y., 2018. "The Lower Regression Function and Testing Expectation Dependence Dominance Hypotheses," Cambridge Working Papers in Economics 1880, Faculty of Economics, University of Cambridge.
    10. Le-Yu Chen & Yu-Min Yen, 2021. "Estimations of the Conditional Tail Average Treatment Effect," Papers 2109.08793, arXiv.org, revised Sep 2021.
    11. Juan Carlos Escanciano, 2020. "Uniform Rates for Kernel Estimators of Weakly Dependent Data," Papers 2005.09951, arXiv.org.
    12. Steven Kou & Xianhua Peng, 2016. "On the Measurement of Economic Tail Risk," Operations Research, INFORMS, vol. 64(5), pages 1056-1072, October.
    13. Tomasz Olma, 2021. "Nonparametric Estimation of Truncated Conditional Expectation Functions," Papers 2109.06150, arXiv.org.

  43. Kong, Efang & Linton, Oliver & Xia, Yingcun, 2013. "Global Bahadur Representation For Nonparametric Censored Regression Quantiles And Its Applications," Econometric Theory, Cambridge University Press, vol. 29(5), pages 941-968, October.
    See citations under working paper version above.
  44. Gordon Anderson & Oliver Linton & Teng Leo, 2012. "A polarization-cohesion perspective on cross-country convergence," Journal of Economic Growth, Springer, vol. 17(1), pages 49-69, March.

    Cited by:

    1. Gordon Anderson, Alessio Farcomeni, Maria Grazia Pittau and Roberto Zelli, 2019. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development Since 1990," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(1), pages 1-22, March.
    2. Park, Seonyoung & Shin, Donggyun, 2020. "Recent Changes in the Nature of Distribution Dynamics of US County Incomes," Working Paper Series 20926, Victoria University of Wellington, School of Economics and Finance.
    3. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2013. "On the Determinants of Distribution Dynamics," Discussion Papers 2013/165, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    4. Thanasis Stengos & M. Ege Yazgan & Harun Özkan, 2018. "Persistence In Convergence And Club Formation," Bulletin of Economic Research, Wiley Blackwell, vol. 70(2), pages 119-138, April.
    5. Khan, Haider & Schettino, Francesco & Gabriele, Alberto, 2017. "Polarization and the Middle Class in China: a Non-Parametric Evaluation Using CHNS and CHIP Data," MPRA Paper 85555, University Library of Munich, Germany.
    6. Davide Fiaschi & Lisa Gianmoena & Angela Parenti, 2015. "Spatial Clubs in European Regions," Discussion Papers 2015/196, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    7. Gordon Anderson & Teng Wah Leo, 2014. "Ranking Alternative Non-Combinable Prospects: A Stochastic Dominance Based Route to the Second Best Solution," Working Papers tecipa-520, University of Toronto, Department of Economics.
    8. Fiaschi, Davide & Gianmoena, Lisa & Parenti, Angela, 2018. "Spatial club dynamics in European regions," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 115-130.
    9. Breinlich, Holger & Ottaviano, Gianmarco I P & Temple, Jonathan R, 2013. "Regional Growth and Regional Decline," Economics Discussion Papers 8977, University of Essex, Department of Economics.
    10. Clementi, F. & Schettino, F., 2013. "Income polarization in Brazil, 2001-2011: A distributional analysis using PNAD data," 2013 Second Congress, June 6-7, 2013, Parma, Italy 149891, Italian Association of Agricultural and Applied Economics (AIEAA).
    11. Christophe Muller, 2017. "Ethnic Horizontal Inequity in Indonesia," AMSE Working Papers 1715, Aix-Marseille School of Economics, France.
    12. F. Clementi & A. L. Dabalen & V. Molini & F. Schettino, 2017. "When the Centre Cannot Hold: Patterns of Polarization in Nigeria," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 608-632, December.
    13. Gordon Anderson & Alessio Farcomeni & Grazia Pittau & Roberto Zelli, 2014. "A new approach to measuring and studying the characteristics of class membership: The progress of poverty, inequality and polarization of income classes in urban China," Working Papers tecipa-521, University of Toronto, Department of Economics.
    14. Khan, Haider & Schettino, Francesco, 2018. "Income Polarization in the USA (1983-2016): what happened to the middle class?," MPRA Paper 85554, University Library of Munich, Germany.
    15. Schettino, Francesco & Khan, Haider A., 2020. "Income polarization in the USA: What happened to the middle class in the last few decades?," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 149-161.
    16. Gordon Anderson, 2018. "Measuring Aspects of Mobility, Polarization and Convergence in the Absence of Cardinality: Indices Based Upon Transitional Typology," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(3), pages 887-907, October.
    17. Gordon Anderson & Maria Pittau & Roberto Zelli, 2014. "Poverty status probability: a new approach to measuring poverty and the progress of the poor," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(4), pages 469-488, December.
    18. Anderson, Gordon & Farcomeni, Alessio & Pittau, Maria Grazia & Zelli, Roberto, 2016. "A new approach to measuring and studying the characteristics of class membership: Examining poverty, inequality and polarization in urban China," Journal of Econometrics, Elsevier, vol. 191(2), pages 348-359.
    19. Christophe Muller, 2016. "Ethnic inequality and community activities in Indonesia," WIDER Working Paper Series wp-2016-170, World Institute for Development Economic Research (UNU-WIDER).

  45. Anderson, Gordon & Linton, Oliver & Whang, Yoon-Jae, 2012. "Nonparametric estimation and inference about the overlap of two distributions," Journal of Econometrics, Elsevier, vol. 171(1), pages 1-23.

    Cited by:

    1. Anderson, G. & Linton, O. & Pittau, M G. & Whang, Y-J. & Zelli, R., 2020. "On Unit Free Assessment of The Extent of Multilateral Distributional Variation," Cambridge Working Papers in Economics 20123, Faculty of Economics, University of Cambridge.
    2. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers CWP09/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Anderson, Gordon & Leo, Teng Wah, 2013. "An empirical examination of matching theories: The one child policy, partner choice and matching intensity in urban China," Journal of Comparative Economics, Elsevier, vol. 41(2), pages 468-489.
    4. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    5. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers CWP53/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers 53/13, Institute for Fiscal Studies.
    7. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    8. Maria Grazia Pittau & Roberto Zelli, 2017. "At the roots of Gini’s transvariation: extracts from “Il concetto di transvariazione e le sue prime applicazioni”," METRON, Springer;Sapienza Università di Roma, vol. 75(2), pages 127-140, August.
    9. Lee, Sokbae & Song, Kyungchul & Whang, Yoon-Jae, 2013. "Testing functional inequalities," Journal of Econometrics, Elsevier, vol. 172(1), pages 14-32.
    10. Christian M Dahl & Martin Huber & Giovanni Mellace, 2023. "It is never too LATE: a new look at local average treatment effects with or without defiers," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 378-404.
    11. Gordon Anderson & Oliver Linton & Jasmin Thomas, 2017. "Similarity, dissimilarity and exceptionality: generalizing Gini’s transvariation to measure “differentness” in many distributions," METRON, Springer;Sapienza Università di Roma, vol. 75(2), pages 161-180, August.
    12. Chemeris, Anna & Liu, Yong & Ker, Alan P., 2022. "Insurance subsidies, climate change, and innovation: Implications for crop yield resiliency," Food Policy, Elsevier, vol. 108(C).
    13. Maribel Jiménez & Mónica Jiménez, 2019. "Intergenerational educational mobility in Latin America. An analysis from the equal opportunity approach," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 38(76), pages 289-330, January.
    14. Gordon Anderson & Alessio Farcomeni & Grazia Pittau & Roberto Zelli, 2014. "A new approach to measuring and studying the characteristics of class membership: The progress of poverty, inequality and polarization of income classes in urban China," Working Papers tecipa-521, University of Toronto, Department of Economics.
    15. François Gerard & Miikka Rokkanen & Christoph Rothe, 2020. "Bounds on treatment effects in regression discontinuity designs with a manipulated running variable," Quantitative Economics, Econometric Society, vol. 11(3), pages 839-870, July.
    16. Gordon Anderson & Teng Leo & Robert Muelhaupt, 2014. "Measuring Advances in Equality of Opportunity: The Changing Gender Gap in Educational Attainment in Canada in the Last Half Century," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 119(1), pages 73-99, October.
    17. Wang, Dan & Tian, Lili, 2017. "Parametric methods for confidence interval estimation of overlap coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 12-26.
    18. Gerard, François & Rothe, Christoph & Rokkanen, Miikka, 2016. "Bounds on Treatment Effects in Regression Discontinuity Designs under Manipulation of the Running Variable, with an Application," CEPR Discussion Papers 11668, C.E.P.R. Discussion Papers.
    19. Gordon Anderson & Maria Pittau & Roberto Zelli, 2014. "Poverty status probability: a new approach to measuring poverty and the progress of the poor," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(4), pages 469-488, December.
    20. Anderson, Gordon & Farcomeni, Alessio & Pittau, Maria Grazia & Zelli, Roberto, 2016. "A new approach to measuring and studying the characteristics of class membership: Examining poverty, inequality and polarization in urban China," Journal of Econometrics, Elsevier, vol. 191(2), pages 348-359.
    21. Gordon Anderson & Maria Grazia Pittau & Roberto Zelli, 2020. "Measuring the progress of equality of educational opportunity in absence of cardinal comparability," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 155-174, August.
    22. Anderson, Gordon & Fu, Rui & Leo, Teng Wah, 2022. "Health, loneliness and the ageing process in the absence of cardinal measure: Rendering intangibles tangible," The Journal of the Economics of Ageing, Elsevier, vol. 22(C).
    23. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers 09/14, Institute for Fiscal Studies.
    24. Seo, Juwon, 2018. "Tests of stochastic monotonicity with improved power," Journal of Econometrics, Elsevier, vol. 207(1), pages 53-70.
    25. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

  46. Srisuma, Sorawoot & Linton, Oliver, 2012. "Semiparametric estimation of Markov decision processes with continuous state space," Journal of Econometrics, Elsevier, vol. 166(2), pages 320-341.
    See citations under working paper version above.
  47. Koo, Bonsoo & Linton, Oliver, 2012. "Estimation of semiparametric locally stationary diffusion models," Journal of Econometrics, Elsevier, vol. 170(1), pages 210-233.

    Cited by:

    1. Dong, Chaohua & Linton, Oliver, 2018. "Additive nonparametric models with time variable and both stationary and nonstationary regressors," Journal of Econometrics, Elsevier, vol. 207(1), pages 212-236.
    2. Frazier, David T. & Koo, Bonsoo, 2021. "Indirect inference for locally stationary models," Journal of Econometrics, Elsevier, vol. 223(1), pages 1-27.
    3. Boonsoo Koo & Myung Hwan Seo, 2013. "Structural-break models under mis-specification: implications for forecasting," Monash Econometrics and Business Statistics Working Papers 11/13, Monash University, Department of Econometrics and Business Statistics.
    4. David T. Frazier & Bonsoo Koo, 2020. "Indirect Inference for Locally Stationary Models," Monash Econometrics and Business Statistics Working Papers 30/20, Monash University, Department of Econometrics and Business Statistics.
    5. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
    6. Ruijun Bu & Jihyun Kim & Bin Wang, 2020. "Uniform and Lp Convergences of Nonparametric Estimation for Diffusion Models," Working Papers 202021, University of Liverpool, Department of Economics.
    7. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    8. Bonsoo Koo & Oliver Linton, 2013. "Let's get LADE: robust estimation of semiparametric multiplicative volatility models," CeMMAP working papers 11/13, Institute for Fiscal Studies.
    9. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.

  48. Gregory Connor & Matthias Hagmann & Oliver Linton, 2012. "Efficient Semiparametric Estimation of the Fama–French Model and Extensions," Econometrica, Econometric Society, vol. 80(2), pages 713-754, March.

    Cited by:

    1. Sakemoto, Ryuta, 2019. "Currency carry trades and the conditional factor model," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 198-208.
    2. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    3. Zhang, Lyuou & Zhou, Wen & Wang, Haonan, 2021. "A semiparametric latent factor model for large scale temporal data with heteroscedasticity," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    4. Fei Liu & Jiti Gao & Yanrong Yang, 2019. "Nonparametric Estimation in Panel Data Models with Heterogeneity and Time Varyingness," Monash Econometrics and Business Statistics Working Papers 24/19, Monash University, Department of Econometrics and Business Statistics.
    5. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    6. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "Estimation of large dimensional conditional factor models in finance," Working Papers unige:125031, University of Geneva, Geneva School of Economics and Management.
    7. Stefano Giglio & Dacheng Xiu, 2017. "Inference on Risk Premia in the Presence of Omitted Factors," NBER Working Papers 23527, National Bureau of Economic Research, Inc.
    8. Ossola, Elisa & Gagilardini, Patrick & Scaillet, Olivier, 2015. "Time-varying risk premium in large cross-sectional equity datasets," Working Papers unige:76321, University of Geneva, Geneva School of Economics and Management.
    9. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Characteristic Effects of Large Continuous-Time Linear Models," Papers 1711.04392, arXiv.org, revised Dec 2018.
    11. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Papers 2012.03182, arXiv.org, revised Nov 2021.
    12. Ilze KALNINA, 2015. "Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas," Cahiers de recherche 13-2015, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    13. Kunpeng Li & Qi Li & Lina Lu, 2018. "Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models," Supervisory Research and Analysis Working Papers RPA 18-2, Federal Reserve Bank of Boston.
    14. Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "A diagnostic criterion for approximate factor structure," Papers 1612.04990, arXiv.org, revised Aug 2017.
    15. Sainan Jin & Liangjun Su & Yonghui Zhang, 2015. "Nonparametric testing for anomaly effects in empirical asset pricing models," Empirical Economics, Springer, vol. 48(1), pages 9-36, February.
    16. French, Declan & Wu, Yuliang & Li, Youwei, 2016. "Identifying the relative importance of stock characteristics," Journal of Multinational Financial Management, Elsevier, vol. 34(C), pages 80-91.
    17. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    18. Jaeheon Jung, 2019. "Estimating a Large Covariance Matrix in Time-varying Factor Models," Papers 1910.11965, arXiv.org.
    19. Dolado, Juan J & Chen, Liang & Gonzalo, Jesus & Pan, Haozi, 2023. "Estimation of Characteristics-based Quantile Factor Models," CEPR Discussion Papers 18115, C.E.P.R. Discussion Papers.
    20. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    21. Jahn, Malte, 2020. "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, vol. 91(C), pages 148-154.
    22. Xiang, Jingjie & Li, Kunpeng & Cui, Guowei, 2018. "A note on the asymptotic properties of least squares estimation in high dimensional constrained factor models," Economics Letters, Elsevier, vol. 171(C), pages 144-148.
    23. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.
    24. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2018. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 7187, CESifo.
    25. Chaieb, Ines & Langlois, Hugues & Scaillet, Olivier, 2021. "Factors and risk premia in individual international stock returns," Journal of Financial Economics, Elsevier, vol. 141(2), pages 669-692.
    26. Güler ARAS & İlhan ÇAM & Bilal ZAVALSIZ & Serkan KESKİN, 2018. "Fama-French Çok Faktör Varlık Fiyatlama Modellerinin Performanslarının Karşılaştırılması: Borsa İstanbul Üzerine Bir Uygulama," Istanbul Business Research, Istanbul University Business School, vol. 47(2), pages 183-207, November.
    27. Szymon Borak & Wolfgang Härdle & Enno Mammen & Byeong U. Park, 2007. "Time Series Modelling with Semiparametric Factor Dynamics," SFB 649 Discussion Papers SFB649DP2007-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    28. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas," Departmental Working Papers 201711, Rutgers University, Department of Economics.
    29. Jung, Sungkyu, 2018. "Continuum directions for supervised dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 27-43.
    30. Anshul Verma & Riccardo Junior Buonocore & Tiziana di Matteo, 2017. "A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering," Papers 1712.02138, arXiv.org, revised May 2018.
    31. Fei Liu & Jiti Gao & Yanrong Yang, 2020. "Time-Varying Panel Data Models with an Additive Factor Structure," Monash Econometrics and Business Statistics Working Papers 42/20, Monash University, Department of Econometrics and Business Statistics.
    32. Likai Chen & Weining Wang & Wei Biao Wu, 2017. "Dynamic Semiparametric Factor Model with a Common Break," SFB 649 Discussion Papers SFB649DP2017-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    33. Cai, Zongwu & Ren, Yu & Yang, Bingduo, 2015. "A semiparametric conditional capital asset pricing model," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 117-126.
    34. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.
    35. Jianqing Fan & Yuan Ke & Yuan Liao, 2016. "Augmented Factor Models with Applications to Validating Market Risk Factors and Forecasting Bond Risk Premia," Papers 1603.07041, arXiv.org, revised Sep 2018.
    36. Georg Keilbar & Juan M. Rodriguez-Poo & Alexandra Soberon & Weining Wang, 2022. "A semiparametric approach for interactive fixed effects panel data models," Papers 2201.11482, arXiv.org, revised Mar 2023.
    37. Fan, Jianqing & Xue, Lingzhou & Yao, Jiawei, 2017. "Sufficient forecasting using factor models," Journal of Econometrics, Elsevier, vol. 201(2), pages 292-306.
    38. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    39. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    40. Brownlees, Christian T., 2019. "Hierarchical GARCH," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 17-27.
    41. Jungjun Choi & Hyukjun Kwon & Yuan Liao, 2023. "Inference for Low-rank Models without Estimating the Rank," Papers 2311.16440, arXiv.org.
    42. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    43. Lee, Jiyon, 2015. "A semiparametric single index model with heterogeneous impacts on an unobserved variable," Journal of Econometrics, Elsevier, vol. 184(1), pages 13-36.

  49. Li, Degui & Lu, Zudi & Linton, Oliver, 2012. "Local Linear Fitting Under Near Epoch Dependence: Uniform Consistency With Convergence Rates," Econometric Theory, Cambridge University Press, vol. 28(5), pages 935-958, October.
    See citations under working paper version above.
  50. Kim, Woocheol & Linton, Oliver, 2011. "Estimation Of A Semiparametric Igarch(1,1) Model," Econometric Theory, Cambridge University Press, vol. 27(3), pages 639-661, June.
    See citations under working paper version above.
  51. Buch-Kromann, Tine & Guillén, Montserrat & Linton, Oliver & Nielsen, Jens Perch, 2011. "Multivariate density estimation using dimension reducing information and tail flattening transformations," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 99-110, January.

    Cited by:

    1. Ramon Alemany & Catalina Bolancé & Montserrat Guillén, 2012. "Nonparametric estimation of Value-at-Risk," Working Papers XREAP2012-19, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2012.
    2. Urbina, Jilber & Guillén, Montserrat, 2013. "An application of capital allocation principles to operational risk," MPRA Paper 75726, University Library of Munich, Germany, revised Dec 2013.
    3. María Luz Gámiz & Enno Mammen & María Dolores Martínez Miranda & Jens Perch Nielsen, 2016. "Double one-sided cross-validation of local linear hazards," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 755-779, September.
    4. Gámiz Pérez, M. Luz & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2013. "Smoothing survival densities in practice," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 368-382.
    5. Del Brio, Esther B. & Perote, Javier, 2012. "Gram–Charlier densities: Maximum likelihood versus the method of moments," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 531-537.

  52. Issler, João Victor & Linton, Oliver & Timmermann, Allan, 2011. "Annals issue on forecasting--Guest editors' introduction," Journal of Econometrics, Elsevier, vol. 164(1), pages 1-3, September.

    Cited by:

    1. Mikkel Plagborg-Møller & Christian K. Wolf, 2020. "Local Projections and VARs Estimate the Same Impulse Responses," Working Papers 2020-16, Princeton University. Economics Department..

  53. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    See citations under working paper version above.
  54. Oliver B. Linton & Yang Yan, 2011. "Semi- and Nonparametric ARCH Processes," Journal of Probability and Statistics, Hindawi, vol. 2011, pages 1-17, August.

    Cited by:

    1. Joseph Ngatchou-Wandji & Marwa Ltaifa & Didier Alain Njamen Njomen & Jia Shen, 2022. "Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    2. Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019. "Conditional Variance Forecasts for Long-Term Stock Returns," Risks, MDPI, vol. 7(4), pages 1-22, November.

  55. Atak, Alev & Linton, Oliver & Xiao, Zhijie, 2011. "A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom," Journal of Econometrics, Elsevier, vol. 164(1), pages 92-115, September.
    See citations under working paper version above.
  56. Lewbel, Arthur & McFadden, Daniel & Linton, Oliver, 2011. "Estimating features of a distribution from binomial data," Journal of Econometrics, Elsevier, vol. 162(2), pages 170-188, June.
    See citations under working paper version above.
  57. Linton, Oliver & Song, Kyungchul & Whang, Yoon-Jae, 2010. "An improved bootstrap test of stochastic dominance," Journal of Econometrics, Elsevier, vol. 154(2), pages 186-202, February.
    See citations under working paper version above.
  58. Linton, Oliver & Pan, Jiazhu & Wang, Hui, 2010. "Estimation For A Nonstationary Semi-Strong Garch(1,1) Model With Heavy-Tailed Errors," Econometric Theory, Cambridge University Press, vol. 26(1), pages 1-28, February.

    Cited by:

    1. Chen, Min & Zhu, Ke, 2013. "Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations," MPRA Paper 50487, University Library of Munich, Germany.
    2. Aknouche, Abdelhakim & Al-Eid, Eid M. & Hmeid, Aboubakry M., 2011. "Offline and online weighted least squares estimation of nonstationary power ARCH processes," Statistics & Probability Letters, Elsevier, vol. 81(10), pages 1535-1540, October.
    3. Hill, Jonathan B. & Aguilar, Mike, 2013. "Moment condition tests for heavy tailed time series," Journal of Econometrics, Elsevier, vol. 172(2), pages 255-274.
    4. Hill, Jonathan B. & Prokhorov, Artem, 2016. "GEL estimation for heavy-tailed GARCH models with robust empirical likelihood inference," Journal of Econometrics, Elsevier, vol. 190(1), pages 18-45.
    5. Wang, Hui & Pan, Jiazhu, 2014. "Normal mixture quasi maximum likelihood estimation for non-stationary TGARCH(1,1) models," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 117-123.
    6. Chen, Min & Zhu, Ke, 2015. "Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations," Journal of Econometrics, Elsevier, vol. 189(2), pages 313-320.
    7. Ding, Y., 2021. "Conditional Heteroskedasticity in the Volatility of Asset Returns," Janeway Institute Working Papers 2111, Faculty of Economics, University of Cambridge.
    8. Chen, Min & Zhu, Ke, 2014. "Sign-based specification tests for martingale difference with conditional heteroscedasity," MPRA Paper 56347, University Library of Munich, Germany.
    9. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
    10. Abdelhakim Aknouche, 2012. "Multistage weighted least squares estimation of ARCH processes in the stable and unstable cases," Statistical Inference for Stochastic Processes, Springer, vol. 15(3), pages 241-256, October.
    11. Francq, Christian & Zakoian, Jean-Michel, 2010. "Strict stationarity testing and estimation of explosive ARCH models," MPRA Paper 22414, University Library of Munich, Germany.
    12. Ding, Y., 2021. "Conditional Heteroskedasticity in the Volatility of Asset Returns," Cambridge Working Papers in Economics 2179, Faculty of Economics, University of Cambridge.
    13. Aguilar, Mike & Hill, Jonathan B., 2015. "Robust score and portmanteau tests of volatility spillover," Journal of Econometrics, Elsevier, vol. 184(1), pages 37-61.
    14. Zhu, Ke, 2015. "Hausman tests for the error distribution in conditionally heteroskedastic models," MPRA Paper 66991, University Library of Munich, Germany.
    15. Bonsoo Koo & Oliver Linton, 2013. "Let's get LADE: robust estimation of semiparametric multiplicative volatility models," CeMMAP working papers 11/13, Institute for Fiscal Studies.
    16. Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2016. "Nonstationary GARCH with t-distributed innovations," Economics Letters, Elsevier, vol. 138(C), pages 19-21.

  59. Hafner, Christian M. & Linton, Oliver, 2010. "Efficient estimation of a multivariate multiplicative volatility model," Journal of Econometrics, Elsevier, vol. 159(1), pages 55-73, November.
    See citations under working paper version above.
  60. Kong, Efang & Linton, Oliver & Xia, Yingcun, 2010. "Uniform Bahadur Representation For Local Polynomial Estimates Of M-Regression And Its Application To The Additive Model," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1529-1564, October.
    See citations under working paper version above.
  61. Oliver Linton & David Jacho-Chávez, 2010. "On internally corrected and symmetrized kernel estimators for nonparametric regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 166-186, May.

    Cited by:

    1. Yuliana Linke & Igor Borisov & Pavel Ruzankin & Vladimir Kutsenko & Elena Yarovaya & Svetlana Shalnova, 2022. "Universal Local Linear Kernel Estimators in Nonparametric Regression," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
    2. Qian, Junhui & Wang, Le, 2012. "Estimating semiparametric panel data models by marginal integration," Journal of Econometrics, Elsevier, vol. 167(2), pages 483-493.
    3. Shen, Jia & Xie, Yuan, 2013. "Strong consistency of the internal estimator of nonparametric regression with dependent data," Statistics & Probability Letters, Elsevier, vol. 83(8), pages 1915-1925.

  62. Jacho-Chávez, David & Lewbel, Arthur & Linton, Oliver, 2010. "Identification and nonparametric estimation of a transformed additively separable model," Journal of Econometrics, Elsevier, vol. 156(2), pages 392-407, June.
    See citations under working paper version above.
  63. Sokbae Lee & Oliver Linton & Yoon-Jae Whang, 2009. "Testing for Stochastic Monotonicity," Econometrica, Econometric Society, vol. 77(2), pages 585-602, March.
    See citations under working paper version above.
  64. Oliver Linton & Jens Perch Nielsen & Søren Feodor Nielsen, 2009. "Non-parametric regression with a latent time series," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 187-207, July.
    See citations under working paper version above.
  65. Linton, Oliver & Sancetta, Alessio, 2009. "Consistent estimation of a general nonparametric regression function in time series," Journal of Econometrics, Elsevier, vol. 152(1), pages 70-78, September.

    Cited by:

    1. Seok Young Hong & Oliver Linton, 2016. "Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in?finite order," CeMMAP working papers CWP53/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Hong, Seok Young & Linton, Oliver, 2020. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Journal of Econometrics, Elsevier, vol. 219(2), pages 389-424.
    3. Degui Li & Zudi Lu & Oliver Linton, 2011. "Local Linear Fitting Under Near Epoch Dependence: Uniform consistency with Convergence Rates," Monash Econometrics and Business Statistics Working Papers 16/11, Monash University, Department of Econometrics and Business Statistics.
    4. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Degui Li & Oliver Linton & Zudi Lu, 2010. "Loch Linear Fitting under Near Epoch Dependence: Uniform Consistency with Convergence Rate," STICERD - Econometrics Paper Series 549, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    6. Seok Young Hong & Oliver Linton, 2016. "Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in finite order," CeMMAP working papers 53/16, Institute for Fiscal Studies.
    7. Battey, Heather & Sancetta, Alessio, 2013. "Conditional estimation for dependent functional data," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 1-17.
    8. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.

  66. Linton, Oliver B. & Mammen, Enno, 2008. "Nonparametric transformation to white noise," Journal of Econometrics, Elsevier, vol. 142(1), pages 241-264, January.
    See citations under working paper version above.
  67. Kalnina, Ilze & Linton, Oliver, 2008. "Estimating quadratic variation consistently in the presence of endogenous and diurnal measurement error," Journal of Econometrics, Elsevier, vol. 147(1), pages 47-59, November.

    Cited by:

    1. Yuta Koike, 2013. "Limit Theorems for the Pre-averaged Hayashi-Yoshida Estimator with Random Sampling," Global COE Hi-Stat Discussion Paper Series gd12-276, Institute of Economic Research, Hitotsubashi University.
    2. Chen, Richard Y. & Mykland, Per A., 2017. "Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data," Journal of Econometrics, Elsevier, vol. 200(1), pages 79-103.
    3. Chaker, Selma, 2019. "The signal and the noise volatilities," Research in International Business and Finance, Elsevier, vol. 50(C), pages 79-105.
    4. Rasmus Tangsgaard Varneskov, 2011. "Flat-Top Realized Kernel Estimation of Quadratic Covariation with Non-Synchronous and Noisy Asset Prices," CREATES Research Papers 2011-35, Department of Economics and Business Economics, Aarhus University.
    5. Richard Y. Chen & Per A. Mykland, 2015. "Model-Free Approaches to Discern Non-Stationary Microstructure Noise and Time-Varying Liquidity in High-Frequency Data," Papers 1512.06159, arXiv.org, revised Oct 2018.
    6. Christensen, Kim & Oomen, Roel & Renò, Roberto, 2022. "The drift burst hypothesis," Journal of Econometrics, Elsevier, vol. 227(2), pages 461-497.
    7. Wang, Fangfang, 2014. "Optimal design of Fourier estimator in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 708-722.
    8. Clinet, Simon & Potiron, Yoann, 2019. "Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book," Journal of Econometrics, Elsevier, vol. 209(2), pages 289-337.
    9. Li, Yingying & Zhang, Zhiyuan & Zheng, Xinghua, 2013. "Volatility inference in the presence of both endogenous time and microstructure noise," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2696-2727.
    10. Ding, Yashuang (Dexter), 2023. "A simple joint model for returns, volatility and volatility of volatility," Journal of Econometrics, Elsevier, vol. 232(2), pages 521-543.
    11. Ilze KALNINA, 2015. "Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas," Cahiers de recherche 13-2015, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    12. Li, Yifan & Nolte, Ingmar & Vasios, Michalis & Voev, Valeri & Xu, Qi, 2022. "Weighted Least Squares Realized Covariation Estimation," Journal of Banking & Finance, Elsevier, vol. 137(C).
    13. Fangfang Wang, 2016. "An Unbiased Measure of Integrated Volatility in the Frequency Domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 147-164, March.
    14. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    15. Giorgio Mirone, 2017. "Inference from the futures: ranking the noise cancelling accuracy of realized measures," CREATES Research Papers 2017-24, Department of Economics and Business Economics, Aarhus University.
    16. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Janeway Institute Working Papers 2208, Faculty of Economics, University of Cambridge.
    17. Yoann Potiron & Per Mykland, 2016. "Local Parametric Estimation in High Frequency Data," Papers 1603.05700, arXiv.org, revised Aug 2018.
    18. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2009. "Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading," Global COE Hi-Stat Discussion Paper Series gd08-037, Institute of Economic Research, Hitotsubashi University.
    19. Bibinger, Markus & Hautsch, Nikolaus & Malec, Peter & Reiss, Markus, 2014. "Estimating the spot covariation of asset prices: Statistical theory and empirical evidence," CFS Working Paper Series 477, Center for Financial Studies (CFS).
    20. Yacine Aït-Sahalia & Jean Jacod, 2012. "Analyzing the Spectrum of Asset Returns: Jump and Volatility Components in High Frequency Data," Journal of Economic Literature, American Economic Association, vol. 50(4), pages 1007-1050, December.
    21. Neil Shephard & Ole E. Barndorff-Nielsen & Asger Lunde, 2006. "Subsampling realised kernels," Economics Series Working Papers 278, University of Oxford, Department of Economics.
    22. Mykland, Per A. & Zhang, Lan & Chen, Dachuan, 2019. "The algebra of two scales estimation, and the S-TSRV: High frequency estimation that is robust to sampling times," Journal of Econometrics, Elsevier, vol. 208(1), pages 101-119.
    23. Simon Clinet & Yoann Potiron, 2017. "Efficient asymptotic variance reduction when estimating volatility in high frequency data," Papers 1701.01185, arXiv.org, revised Jun 2018.
    24. Zu, Yang & Peter Boswijk, H., 2014. "Estimating spot volatility with high-frequency financial data," Journal of Econometrics, Elsevier, vol. 181(2), pages 117-135.
    25. Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2013. "Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns," CREATES Research Papers 2013-07, Department of Economics and Business Economics, Aarhus University.
    26. Park, Sujin & Hong, Seok Young & Linton, Oliver, 2016. "Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error," Journal of Econometrics, Elsevier, vol. 191(2), pages 325-347.
    27. Li, M. Z. & Linton, O., 2021. "Robust Estimation of Integrated and Spot Volatility," Cambridge Working Papers in Economics 2115, Faculty of Economics, University of Cambridge.
    28. Chaker, Selma, 2017. "On high frequency estimation of the frictionless price: The use of observed liquidity variables," Journal of Econometrics, Elsevier, vol. 201(1), pages 127-143.
    29. Ding, Y., 2021. "Augmented Real-Time GARCH: A Joint Model for Returns, Volatility and Volatility of Volatility," Cambridge Working Papers in Economics 2112, Faculty of Economics, University of Cambridge.
    30. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    31. Andersen, Torben G. & Archakov, Ilya & Cebiroglu, Gökhan & Hautsch, Nikolaus, 2022. "Local mispricing and microstructural noise: A parametric perspective," Journal of Econometrics, Elsevier, vol. 230(2), pages 510-534.
    32. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.
    33. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.
    34. Selma Chaker & Nour Meddahi, 2013. "Volatility Forecasting when the Noise Variance Is Time-Varying," Staff Working Papers 13-48, Bank of Canada.
    35. Jacod, Jean & Li, Yingying & Zheng, Xinghua, 2019. "Estimating the integrated volatility with tick observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 80-100.
    36. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    37. Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011. "Predictive Inference for Integrated Volatility," Departmental Working Papers 201109, Rutgers University, Department of Economics.
    38. Andersen, Torben G. & Cebiroglu, Gökhan & Hautsch, Nikolaus, 2017. "Volatility, information feedback and market microstructure noise: A tale of two regimes," CFS Working Paper Series 569, Center for Financial Studies (CFS).
    39. Bandi, Federico M. & Russell, Jeffrey R. & Yang, Chen, 2008. "Realized volatility forecasting and option pricing," Journal of Econometrics, Elsevier, vol. 147(1), pages 34-46, November.
    40. Aït-Sahalia, Yacine & Fan, Jianqing & Li, Yingying, 2013. "The leverage effect puzzle: Disentangling sources of bias at high frequency," Journal of Financial Economics, Elsevier, vol. 109(1), pages 224-249.
    41. Shin S. Ikeda, 2013. "A Note on the Mixingale Limit Theorem by McLeish (1977)," GRIPS Discussion Papers 13-11, National Graduate Institute for Policy Studies.
    42. Rui Da & Dacheng Xiu, 2021. "When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility," Econometrica, Econometric Society, vol. 89(6), pages 2787-2825, November.
    43. Selma Chaker, 2013. "Volatility and Liquidity Costs," Staff Working Papers 13-29, Bank of Canada.
    44. Kim Christensen & Roel Oomen & Roberto Renò, 2018. "The drift burst hypothesis," CREATES Research Papers 2018-21, Department of Economics and Business Economics, Aarhus University.
    45. Charles S. Bos & Paweł Janus & Siem Jan Koopman, 2012. "Spot Variance Path Estimation and Its Application to High-Frequency Jump Testing," Journal of Financial Econometrics, Oxford University Press, vol. 10(2), pages 354-389, 2012 06.
    46. Rasmus Tangsgaard Varneskov, 2011. "Generalized Flat-Top Realized Kernel Estimation of Ex-Post Variation of Asset Prices Contaminated by Noise," CREATES Research Papers 2011-31, Department of Economics and Business Economics, Aarhus University.
    47. Kim Christensen & Roel Oomen & Roberto Renò, 2016. "The Drift Burst Hypothesis," CREATES Research Papers 2016-28, Department of Economics and Business Economics, Aarhus University.
    48. Mykland, Per A. & Zhang, Lan, 2021. "The Observed Asymptotic Variance: Hard edges, and a regression approach," Journal of Econometrics, Elsevier, vol. 222(1), pages 411-428.
    49. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
    50. Sujin Park & Oliver Linton, 2012. "Estimating the Quadratic Covariation Matrix for an Asynchronously Observed Continuous Time Signal Masked by Additive Noise," FMG Discussion Papers dp703, Financial Markets Group.
    51. Ikeda, Shin S., 2016. "A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous," Journal of Econometrics, Elsevier, vol. 193(1), pages 203-214.
    52. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    53. Kim, Donggyu & Wang, Yazhen & Zou, Jian, 2016. "Asymptotic theory for large volatility matrix estimation based on high-frequency financial data," Stochastic Processes and their Applications, Elsevier, vol. 126(11), pages 3527-3577.
    54. Yiqi Liu & Qiang Liu & Zhi Liu & Deng Ding, 2017. "Determining the integrated volatility via limit order books with multiple records," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1697-1714, November.
    55. Markus Bibinger & Markus Reiss & Nikolaus Hautsch & Peter Malec, 2014. "Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence," SFB 649 Discussion Papers SFB649DP2014-055, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    56. Bibinger, Markus, 2012. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," Stochastic Processes and their Applications, Elsevier, vol. 122(6), pages 2411-2453.
    57. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    58. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.

  68. Lu, Zudi & Linton, Oliver, 2007. "Local Linear Fitting Under Near Epoch Dependence," Econometric Theory, Cambridge University Press, vol. 23(1), pages 37-70, February.

    Cited by:

    1. Jenish, Nazgul, 2012. "Nonparametric spatial regression under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 167(1), pages 224-239.
    2. Degui Li & Zudi Lu & Oliver Linton, 2011. "Local Linear Fitting Under Near Epoch Dependence: Uniform consistency with Convergence Rates," Monash Econometrics and Business Statistics Working Papers 16/11, Monash University, Department of Econometrics and Business Statistics.
    3. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    4. Chen, Xiaohong & Liao, Zhipeng & Sun, Yixiao, 2014. "Sieve inference on possibly misspecified semi-nonparametric time series models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 639-658.
    5. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric Model Averaging of Ultra-High Dimensional Time Series," Discussion Papers 15/18, Department of Economics, University of York.
    6. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers CWP28/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Nikolay Gospodinov & Masayuki Hirukawa, 2008. "Time Series Nonparametric Regression Using Asymmetric Kernels with an Application to Estimation of Scalar Diffusion Processes," CIRJE F-Series CIRJE-F-573, CIRJE, Faculty of Economics, University of Tokyo.
    8. Xiaohong Chen & Zhipeng Liao & Yixiao Sun, 2012. "Sieve Inference on Semi-nonparametric Time Series Models," Cowles Foundation Discussion Papers 1849, Cowles Foundation for Research in Economics, Yale University.
    9. Degui Li & Oliver Linton & Zudi Lu, 2010. "Loch Linear Fitting under Near Epoch Dependence: Uniform Consistency with Convergence Rate," STICERD - Econometrics Paper Series 549, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    10. Yang, Lixiong & Lee, Chingnun & Shie, Fu Shuen, 2014. "How close a relationship does a capital market have with other markets? A reexamination based on the equal variance test," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 198-226.
    11. Hafner, Christian & Linton, Oliver & Wang, Linqi, 2022. "Dynamic Autoregressive Liquidity (DArLiQ)," LIDAM Discussion Papers LFIN 2022002, Université catholique de Louvain, Louvain Finance (LFIN).
    12. Linton, Oliver & Sancetta, Alessio, 2009. "Consistent estimation of a general nonparametric regression function in time series," Journal of Econometrics, Elsevier, vol. 152(1), pages 70-78, September.
    13. Kurisu, Daisuke, 2019. "On nonparametric inference for spatial regression models under domain expanding and infill asymptotics," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    14. Rossi, Barbara & Inoue, Atsushi & Jin, Lu, 2014. "Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," CEPR Discussion Papers 10168, C.E.P.R. Discussion Papers.
    15. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    16. Xiaohong Chen & . . & Yixiao Sun, 2012. "Sieve inference on semi-nonparametric time series models," CeMMAP working papers 06/12, Institute for Fiscal Studies.
    17. Lee, Jiyon, 2015. "A semiparametric single index model with heterogeneous impacts on an unobserved variable," Journal of Econometrics, Elsevier, vol. 184(1), pages 13-36.
    18. Jenish, Nazgul & Prucha, Ingmar R., 2012. "On spatial processes and asymptotic inference under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 170(1), pages 178-190.
    19. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.

  69. Connor, Gregory & Linton, Oliver, 2007. "Semiparametric estimation of a characteristic-based factor model of common stock returns," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 694-717, December.
    See citations under working paper version above.
  70. Seo, Myung Hwan & Linton, Oliver, 2007. "A smoothed least squares estimator for threshold regression models," Journal of Econometrics, Elsevier, vol. 141(2), pages 704-735, December.
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  71. Linton, Oliver & Xiao, Zhijie, 2007. "A Nonparametric Regression Estimator That Adapts To Error Distribution Of Unknown Form," Econometric Theory, Cambridge University Press, vol. 23(3), pages 371-413, June.
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    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    3. Thilo A. Schmitt & Rudi Schafer & Holger Dette & Thomas Guhr, 2015. "Quantile Correlations: Uncovering temporal dependencies in financial time series," Papers 1507.04990, arXiv.org.
    4. Park, Joon Y. & Whang, Yoon-Jae, 2012. "Random walk or chaos: A formal test on the Lyapunov exponent," Journal of Econometrics, Elsevier, vol. 169(1), pages 61-74.
    5. Jozef Barun'ik & Tobias Kley, 2015. "Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables," Papers 1510.06946, arXiv.org, revised Dec 2018.
    6. Ayedi Ahmed & Marjène Gana & Stéphane Goutte & Khaled Guesmi, 2023. "Managing Portfolio Risk During the BREXIT Crisis: A Cross-Quantilogram Analysis of Stock Markets and Commodities Across European Countries, the US, and BRICS," Working Papers halshs-04068651, HAL.
    7. Baumöhl, Eduard & Lyócsa, Štefan, 2017. "Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis," Finance Research Letters, Elsevier, vol. 23(C), pages 152-164.
    8. Arif, Muhammad & Naeem, Muhammad Abubakr & Farid, Saqib & Nepal, Rabindra & Jamasb, Tooraj, 2022. "Diversifier or more? Hedge and safe haven properties of green bonds during COVID-19," Energy Policy, Elsevier, vol. 168(C).
    9. Valdivia Coria, Joab Dan, 2022. "Apalancamiento, ciclo financiero y económico [Leverage, financial and business cycles]," MPRA Paper 116849, University Library of Munich, Germany.
    10. Ngo Thai Hung & Vo Xuan Vinh, 2023. "Asymmetric impact of the COVID-19 pandemic on foreign exchange markets: Evidence from an extreme quantile approach," Economics and Business Letters, Oviedo University Press, vol. 12(1), pages 20-32.
    11. Andrieş, Alin Marius & Ongena, Steven & Sprincean, Nicu & Tunaru, Radu, 2022. "Risk spillovers and interconnectedness between systemically important institutions," Journal of Financial Stability, Elsevier, vol. 58(C).
    12. Boqiang Lin & Tong Su, 2023. "Uncertainties and green bond markets: Evidence from tail dependence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4458-4475, October.
    13. Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series," Cambridge Working Papers in Economics 1452, Faculty of Economics, University of Cambridge.
    14. Juan Carlos Escanciano & Carlos Velasco, 2006. "Testing the Martingale Difference Hypothesis Using Integrated Regression Functions," Faculty Working Papers 06/06, School of Economics and Business Administration, University of Navarra.
    15. John Galbraith & Simon van Norden, 2008. "The Calibration Of Probabilistic Economic Forecasts," Departmental Working Papers 2008-05, McGill University, Department of Economics.
    16. Riza Demirer & Rangan Gupta & Hossein Hassani & Xu Huang, 2020. "Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram," Economies, MDPI, vol. 8(1), pages 1-12, March.
    17. Ashby, M. & Linton, O. B., 2022. "Do Consumption-based Asset Pricing Models Explain Own-history Predictability in Stock Market Returns?," Cambridge Working Papers in Economics 2259, Faculty of Economics, University of Cambridge.
    18. Labidi, Chiaz & Rahman, Md Lutfur & Hedström, Axel & Uddin, Gazi Salah & Bekiros, Stelios, 2018. "Quantile dependence between developed and emerging stock markets aftermath of the global financial crisis," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 179-211.
    19. Giovanni De Luca & Monica Rosciano, 2020. "Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    20. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, New Economic School (NES).
    21. Syed Jawad Hussain Shahzad & Naveed Raza & David Roubaud & Jose Arreola Hernandez & Stelios Bekiros, 2019. "Gold as Safe Haven for G-7 Stocks and Bonds: A Revisit," Post-Print hal-02352004, HAL.
    22. Corbet, Shaen & Katsiampa, Paraskevi & Lau, Chi Keung Marco, 2020. "Measuring quantile dependence and testing directional predictability between Bitcoin, altcoins and traditional financial assets," International Review of Financial Analysis, Elsevier, vol. 71(C).
    23. Zhang, Dongna & Chen, Xihui Haviour & Lau, Chi Keung Marco & Xu, Bing, 2023. "Implications of cryptocurrency energy usage on climate change," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
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    28. Jose Areola Hernandez & Syed Jawad Hussain Shahzad & Gazi Salah Uddin & Sang Hoon Kang, 2019. "Can agricultural and precious metal commodities diversify and hedge extreme downside and upside oil market risk? An extreme quantile approach," Post-Print hal-02159274, HAL.
    29. Stelios Bekiros & Syed Jawad Hussain Shahzad & Jose Arreola-Hernandez & Mobeen Ur Rehman, 2018. "Directional predictability and time-varying spillovers between stock markets and economic cycles," Post-Print hal-01996787, HAL.
    30. Satish Kumar & Rabeh Khalfaoui & Aviral Kumar Tiwari, 2021. "Does geopolitical risk improve the directional predictability from oil to stock returns? Evidence from oil-exporting and oil-importing countries," Post-Print hal-03797578, HAL.
    31. Davis, Richard & Drees, Holger & Segers, Johan & Warchol, Michal, 2018. "Inference on the tail process with application to financial time series modelling," LIDAM Discussion Papers ISBA 2018002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    32. Shahzad, Syed Jawad Hussain & Naifar, Nader & Hammoudeh, Shawkat & Roubaud, David, 2017. "Directional predictability from oil market uncertainty to sovereign credit spreads of oil-exporting countries: Evidence from rolling windows and crossquantilogram analysis," Energy Economics, Elsevier, vol. 68(C), pages 327-339.
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    35. Rahman, Md Lutfur & Hedström, Axel & Uddin, Gazi Salah & Kang, Sang Hoon, 2021. "Quantile relationship between Islamic and non-Islamic equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    36. Damek, Ewa & Mikosch, Thomas & Zhao, Yuwei & Zienkiewicz, Jacek, 2023. "Whittle estimation based on the extremal spectral density of a heavy-tailed random field," Stochastic Processes and their Applications, Elsevier, vol. 155(C), pages 232-267.
    37. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.
    38. Urom, C. & Mzoughi, Hela & Ndubuisi, Gideon & Guesmi, K., 2022. "Dynamic dependence between clean investments and economic policy uncertainty," MERIT Working Papers 2022-027, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    39. Stefan Birr & Stanislav Volgushev & Tobias Kley & Holger Dette & Marc Hallin, 2017. "Quantile spectral analysis for locally stationary time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1619-1643, November.
    40. Bruno Spilak & Wolfgang Karl Härdle, 2022. "Tail-Risk Protection: Machine Learning Meets Modern Econometrics," Springer Books, in: Cheng-Few Lee & Alice C. Lee (ed.), Encyclopedia of Finance, edition 0, chapter 92, pages 2177-2211, Springer.
    41. Mudassar Hasan & Muhammad Abubakr Naeem & Muhammad Arif & Syed Jawad Hussain Shahzad & Safwan Mohd Nor, 2020. "Geopolitical Risk and Tourism Stocks of Emerging Economies," Sustainability, MDPI, vol. 12(21), pages 1-21, November.
    42. Razzaq, Asif & Sharif, Arshian & An, Hui & Aloui, Chaker, 2022. "Testing the directional predictability between carbon trading and sectoral stocks in China: New insights using cross-quantilogram and rolling window causality approaches," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    43. Engin Bekar, 2022. "The Relationship Between Geopolitical Risks and Housing Returns in Türkiye: Evidence from the Cross – Quantilogram," International Econometric Review (IER), Econometric Research Association, vol. 14(2), pages 59-71, June.
    44. Naeem, Muhammad Abubakr & Qureshi, Fiza & Arif, Muhammad & Balli, Faruk, 2021. "Asymmetric relationship between gold and Islamic stocks in bearish, normal and bullish market conditions," Resources Policy, Elsevier, vol. 72(C).
    45. Ćmiel, Bogdan & Ledwina, Teresa, 2020. "Validation of association," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 55-67.
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    47. Lin Han & Ivor Cribben & Stefan Trueck, 2022. "Extremal Dependence in Australian Electricity Markets," Papers 2202.09970, arXiv.org.
    48. Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.
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    50. Busettti, F. & Harvey, A., 2007. "Tests of time-invariance," Cambridge Working Papers in Economics 0701, Faculty of Economics, University of Cambridge.
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    53. Zheng, Yingfei & Shen, Anran & Li, Ruihai & Yang, Yuhong & Wang, Shengjin & Cheng, Lee-Young, 2023. "Spillover effects between internet financial industry and traditional financial industry: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    54. Avik Sinha & Arshian Sharif & Arnab Adhikari & Ankit Sharma, 2022. "Dependence structure between Indian financial market and energy commodities: a cross-quantilogram based evidence," Annals of Operations Research, Springer, vol. 313(1), pages 257-287, June.
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    56. Bouri, Elie & Shahzad, Syed Jawad Hussain & Raza, Naveed & Roubaud, David, 2018. "Oil volatility and sovereign risk of BRICS," Energy Economics, Elsevier, vol. 70(C), pages 258-269.
    57. Bruno Spilak & Wolfgang Karl Hardle, 2020. "Tail-risk protection: Machine Learning meets modern Econometrics," Papers 2010.03315, arXiv.org, revised Aug 2021.
    58. Yuichi Goto & Tobias Kley & Ria Van Hecke & Stanislav Volgushev & Holger Dette & Marc Hallin, 2021. "The Integrated Copula Spectrum," Working Papers ECARES 2021-29, ULB -- Universite Libre de Bruxelles.
    59. Jungmo Yoon & Antonio F. Galvao, 2020. "Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects," Quantitative Economics, Econometric Society, vol. 11(2), pages 579-608, May.
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    62. Todorova, Neda, 2017. "The intraday directional predictability of large Australian stocks: A cross-quantilogram analysis," Economic Modelling, Elsevier, vol. 64(C), pages 221-230.
    63. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    64. Holger Dette & Marc Hallin & Tobias Kley & Stanislav Volgushev, 2011. "Of Copulas, Quantiles, Ranks and Spectra - An L1-Approach to Spectral Analysis," Working Papers ECARES ECARES 2011-038, ULB -- Universite Libre de Bruxelles.
    65. Chishti, Muhammad Zubair & Khalid, Ali Awais & Sana, Moniba, 2023. "Conflict vs sustainability of global energy, agricultural and metal markets: A lesson from Ukraine-Russia war," Resources Policy, Elsevier, vol. 84(C).
    66. Shen, Yifan & Shi, Xunpeng & Variam, Hari Malamakkavu Padinjare, 2018. "Risk transmission mechanism between energy markets: A VAR for VaR approach," Energy Economics, Elsevier, vol. 75(C), pages 377-388.
    67. Shahzad, Syed Jawad Hussain & Hernandez, Jose Areola & Rehman, Mobeen Ur & Al-Yahyaee, Khamis Hamed & Zakaria, Muhammad, 2018. "A global network topology of stock markets: Transmitters and receivers of spillover effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2136-2153.
    68. Thilo A. Schmitt & Rudi Schäfer & Holger Dette & Thomas Guhr, 2015. "Quantile Correlations: Uncovering Temporal Dependencies In Financial Time Series," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(07), pages 1-16, November.
    69. Shahzad, Syed Jawad Hussain & Rahman, Md Lutfur & Lucey, Brian M. & Uddin, Gazi Salah, 2021. "Re-examining the real option characteristics of gold for gold mining companies," Resources Policy, Elsevier, vol. 70(C).
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  73. Iglesias, Emma M. & Linton, Oliver B., 2007. "Higher Order Asymptotic Theory When A Parameter Is On A Boundary With An Application To Garch Models," Econometric Theory, Cambridge University Press, vol. 23(6), pages 1136-1161, December.

    Cited by:

    1. Feiyu Jiang & Dong Li & Ke Zhu, 2019. "Non-standard inference for augmented double autoregressive models with null volatility coefficients," Papers 1905.01798, arXiv.org.
    2. Jiang, Feiyu & Li, Dong & Zhu, Ke, 2020. "Non-standard inference for augmented double autoregressive models with null volatility coefficients," Journal of Econometrics, Elsevier, vol. 215(1), pages 165-183.
    3. Antonis Demos & Dimitra Kyriakopoulou, 2011. "Bias Correction of ML and QML Estimators in the EGARCH(1,1) Model," DEOS Working Papers 1108, Athens University of Economics and Business.
    4. Christian Francq & Jean-Michel Zakoïan, 2008. "Estimating ARCH Models when the Coefficients are Allowed to be Equal to Zero," Working Papers 2008-07, Center for Research in Economics and Statistics.
    5. DEMOS Antonis, & KYRIAKOPOULOU Dimitra,, 2018. "Finite sample theory and bias correction of maximum likelihood estimators in the EGARCH model," LIDAM Discussion Papers CORE 2018007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Stelios Arvanitis & Antonis Demos, 2014. "A Class of Indirect Inference Estimators: Higher Order Asymptotics and Approximate Bias Correction (Revised)," DEOS Working Papers 1411, Athens University of Economics and Business, revised 23 Sep 2014.
    7. Antonis Demos & Stelios Arvanitis, 2010. "A New Class of Indirect Estimators and Bias Correction," DEOS Working Papers 1023, Athens University of Economics and Business.
    8. Fan, Yanqin & Park, Sang Soo, 2014. "Nonparametric inference for counterfactual means: Bias-correction, confidence sets, and weak IV," Journal of Econometrics, Elsevier, vol. 178(P1), pages 45-56.
    9. Arvanitis Stelios & Demos Antonis, 2014. "Valid Locally Uniform Edgeworth Expansions for a Class of Weakly Dependent Processes or Sequences of Smooth Transformations," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 1-53, July.

  74. Fernandes, Marcelo & Linton, Oliver & Scaillet, Olivier, 2007. "Semiparametric methods in econometrics," Journal of Econometrics, Elsevier, vol. 141(1), pages 1-4, November.

    Cited by:

    1. Hallin, Marc & La Vecchia, Davide, 2017. "R-estimation in semiparametric dynamic location-scale models," Journal of Econometrics, Elsevier, vol. 196(2), pages 233-247.

  75. Cho, Young-Hyun & Linton, Oliver & Whang, Yoon-Jae, 2007. "Are there Monday effects in stock returns: A stochastic dominance approach," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 736-755, December.
    See citations under working paper version above.
  76. Arthur Lewbel & Oliver Linton, 2007. "Nonparametric Matching and Efficient Estimators of Homothetically Separable Functions," Econometrica, Econometric Society, vol. 75(4), pages 1209-1227, July.
    See citations under working paper version above.
  77. Connor, Gregory & Korajczyk, Robert A. & Linton, Oliver, 2006. "The common and specific components of dynamic volatility," Journal of Econometrics, Elsevier, vol. 132(1), pages 231-255, May.

    Cited by:

    1. Gilles Dufrénot & Valérie Mignon & Anne Péguin-Feissolle, 2012. "The effects of the subprime crisis on the Latin American financial markets: an empirical assessment," Post-Print hal-01411539, HAL.
    2. Najam Iqbal & Muhammad Saqib Manzoor & Muhammad Ishaq Bhatti, 2021. "Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19," JRFM, MDPI, vol. 14(7), pages 1-15, July.
    3. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Sep 2023.
    4. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    5. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    6. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    7. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Working Papers ECARES 2023-15, ULB -- Universite Libre de Bruxelles.
    8. Barigozzi, Matteo & Hallin, Mark, 2015. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," LSE Research Online Documents on Economics 60980, London School of Economics and Political Science, LSE Library.
    9. M. Hashem Pesaran & Paolo Zaffaroni, 2009. "Optimality and Diversifiability of Mean Variance and Arbitrage Pricing Portfolios," CESifo Working Paper Series 2857, CESifo.
    10. Pesaran, M.H. & Zaffaroni, P., 2008. "Optimal Asset Allocation with Factor Models for Large Portfolios," Cambridge Working Papers in Economics 0813, Faculty of Economics, University of Cambridge.
    11. Engle, Robert F. & Campos-Martins, Susana, 2023. "What are the events that shake our world? Measuring and hedging global COVOL," Journal of Financial Economics, Elsevier, vol. 147(1), pages 221-242.
    12. Matteo Barigozzi & Marc Hallin, 2018. "Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals," Papers 1811.10045, arXiv.org, revised Jul 2019.
    13. Kim, Donggyu & Fan, Jianqing, 2019. "Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction," Journal of Econometrics, Elsevier, vol. 208(2), pages 395-417.
    14. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series," LEM Papers Series 2006/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    15. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Papers 1510.05118, arXiv.org, revised Jul 2016.
    16. Eric Renault & Thijs Van Der & Bas J M Werker, 2023. "Arbitrage Pricing Theory for Idiosyncratic Variance Factors," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1403-1442.
    17. L. Vanessa Smith & Takashi Yamagata, 2008. "Firm Level Volatility-Return Analysis using Dynamic Panels," Discussion Papers 08/09, Department of Economics, University of York.
    18. Brownlees, Christian T., 2019. "Hierarchical GARCH," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 17-27.
    19. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.
    20. Byun, Sung Je, 2016. "The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 162-180.
    21. Umberto Triacca & Fulvia Focker, 2014. "Estimating overnight volatility of asset returns by using the generalized dynamic factor model approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 235-254, October.
    22. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
    23. Ghysels, Eric, 2014. "Factor Analysis with Large Panels of Volatility Proxies," CEPR Discussion Papers 10034, C.E.P.R. Discussion Papers.
    24. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2011. "Forecasting electricity prices and their volatilities using Unobserved Components," Energy Economics, Elsevier, vol. 33(6), pages 1227-1239.
    25. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2021. "Next Generation Models for Portfolio Risk Management: An Approach Using Financial Big Data," Papers 2102.12783, arXiv.org, revised Feb 2022.
    26. Lübbers, Johannes & Posch, Peter N., 2016. "Commodities' common factor: An empirical assessment of the markets' drivers," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 28-40.

  78. Andrew Jeffrey & Oliver Linton & Thong Nguyen, 2006. "Flexible Term Structure Estimation: Which Method is Preferred?," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 63(1), pages 99-122, February.
    See citations under working paper version above.
  79. Kristensen, Dennis & Linton, Oliver, 2006. "A Closed-Form Estimator For The Garch(1,1) Model," Econometric Theory, Cambridge University Press, vol. 22(2), pages 323-337, April.

    Cited by:

    1. Arvanitis Stelios & Demos Antonis, 2018. "On the Validity of Edgeworth Expansions and Moment Approximations for Three Indirect Inference Estimators," Journal of Econometric Methods, De Gruyter, vol. 7(1), pages 1-38, January.
    2. Todd, Prono, 2009. "Simple, Skewness-Based GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 30994, University Library of Munich, Germany, revised 30 Jul 2011.
    3. Donald W. K. Andrews & Patrik Guggenberger, 2014. "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 376-381, May.
    4. Hafner, C. & Preminger, A., 2010. "Deciding between GARCH and Stochastic Volatility via Strong Decision Rules," LIDAM Reprints ISBA 2010032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Hafner, Christian & Linton, Oliver, 2017. "An Almost Closed Form Estimator For The EGARCH Model," LIDAM Reprints ISBA 2017040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. PREMINGER, Arie & STORTI, Giuseppe, 2006. "A GARCH (1,1) estimator with (almost) no moment conditions on the error term," LIDAM Discussion Papers CORE 2006068, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Prono, Todd, 2011. "When A Factor Is Measured with Error: The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Factor Models," MPRA Paper 33593, University Library of Munich, Germany.
    8. HAFNER, Christian & PREMINGER, Arie, 2006. "Asymptotic theory for a factor GARCH model," LIDAM Discussion Papers CORE 2006071, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
    10. Leucht, Anne & Neumann, Michael H. & Kreiss, Jens-Peter, 2013. "A model specification test for GARCH(1,1) processes," Working Papers 13-11, University of Mannheim, Department of Economics.
    11. Abhimanyu Gupta, 2020. "Efficient closed-form estimation of large spatial autoregressions," Papers 2008.12395, arXiv.org, revised May 2021.
    12. Amendola, Alessandra & Storti, Giuseppe, 2008. "A GMM procedure for combining volatility forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3047-3060, February.
    13. Todd Prono, 2016. "Closed-Form Estimation of Finite-Order ARCH Models: Asymptotic Theory and Finite-Sample Performance," Finance and Economics Discussion Series 2016-083, Board of Governors of the Federal Reserve System (U.S.).
    14. Hafner C. & Linton, O., 2013. "An Almost Closed Form Estimator for the EGARCH," LIDAM Discussion Papers ISBA 2013010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Alessandra Amendola & Giuseppe Storti, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers SFB649DP2009-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    16. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707.
    17. Christian M. Dahl & Emma M. Iglesias, 2008. "The limiting properties of the QMLE in a general class of asymmetric volatility models," CREATES Research Papers 2008-38, Department of Economics and Business Economics, Aarhus University.
    18. Shi, Yanlin, 2022. "A closed-form estimator for the Markov switching in mean model," Finance Research Letters, Elsevier, vol. 44(C).
    19. Todd Prono, 2017. "Regular Variation of Popular GARCH Processes Allowing for Distributional Asymmetry," Finance and Economics Discussion Series 2017-095, Board of Governors of the Federal Reserve System (U.S.).
    20. Li, Qi & Lian, Heng & Zhu, Fukang, 2016. "Robust closed-form estimators for the integer-valued GARCH (1,1) model," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 209-225.
    21. Sbrana, Giacomo & Poloni, Federico, 2013. "A closed-form estimator for the multivariate GARCH(1,1) model," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 152-162.
    22. Ahsan, Md. Nazmul & Dufour, Jean-Marie, 2021. "Simple estimators and inference for higher-order stochastic volatility models," Journal of Econometrics, Elsevier, vol. 224(1), pages 181-197.
    23. Xuejie Feng & Chiping Zhang, 2020. "A Perturbation Method to Optimize the Parameters of Autoregressive Conditional Heteroscedasticity Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 1021-1044, March.
    24. Poloni, Federico & Sbrana, Giacomo, 2014. "Feasible generalized least squares estimation of multivariate GARCH(1, 1) models," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 151-159.

  80. Linton, Oliver, 2005. "Nonparametric Inference For Unbalanced Time Series Data," Econometric Theory, Cambridge University Press, vol. 21(1), pages 143-157, February.
    See citations under working paper version above.
  81. O. Linton & E. Mammen, 2005. "Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods," Econometrica, Econometric Society, vol. 73(3), pages 771-836, May.
    See citations under working paper version above.
  82. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2005. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 735-765.
    See citations under working paper version above.
  83. Douglas J. Hodgson & Oliver Linton & Keith Vorkink, 2004. "Testing forward exchange rate unbiasedness efficiently: a semiparametric approach," Journal of Applied Economics, Universidad del CEMA, vol. 7, pages 325-353, November.

    Cited by:

    1. Dhekra Azouzi & Rohit Vishal Kumar & Chaker Aloui, 2011. "Forward Rate Unbiasedness Hypothesis in the Tunisian Exchange Rate Market," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 1(2), pages 17-44, July.
    2. Amengual, Dante & Sentana, Enrique, 2010. "A comparison of mean-variance efficiency tests," Journal of Econometrics, Elsevier, vol. 154(1), pages 16-34, January.
    3. Ioannis N. Kallianiotis, 2021. "Exchange Rate Determination: The Portfolio-Balance Approach," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 11(1), pages 1-2.
    4. Fu, Hsuan & Luger, Richard, 2022. "Multiple testing of the forward rate unbiasedness hypothesis across currencies," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 232-245.

  84. Kim, Woocheol & Linton, Oliver, 2004. "The Live Method For Generalized Additive Volatility Models," Econometric Theory, Cambridge University Press, vol. 20(6), pages 1094-1139, December.
    See citations under working paper version above.
  85. Steve Berry & Oliver B. Linton & Ariel Pakes, 2004. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(3), pages 613-654.
    See citations under working paper version above.
  86. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.
    See citations under working paper version above.
  87. Kristensen, Dennis & Linton, Oliver, 2004. "03.5.2. Consistent Standard Errors for Target Variance Approach to GARCH Estimation—Solution," Econometric Theory, Cambridge University Press, vol. 20(5), pages 990-993, October.

    Cited by:

    1. Rasmus Søndergaard Pedersen, 2014. "Targeting estimation of CCC-Garch models with infinite fourth moments," Discussion Papers 14-04, University of Copenhagen. Department of Economics.
    2. Francq, Christian & Horvath, Lajos & Zakoian, Jean-Michel, 2009. "Merits and drawbacks of variance targeting in GARCH models," MPRA Paper 15143, University Library of Munich, Germany.
    3. Christian Francq & Lajos Horváth & Jean-Michel Zakoïan, 2016. "Variance Targeting Estimation of Multivariate GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 353-382.
    4. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    5. Qi Li & Fukang Zhu, 2020. "Mean targeting estimator for the integer-valued GARCH(1, 1) model," Statistical Papers, Springer, vol. 61(2), pages 659-679, April.
    6. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    7. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula–Based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics, MDPI, vol. 5(2), pages 1-24, April.
    8. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    9. Stanislav Anatolyev & Stanislav Khrapov, 2015. "Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting," Econometrics, MDPI, vol. 3(3), pages 1-23, August.
    10. Van Dijk, Dick & Munandar, Haris & Hafner, Christian, 2011. "The Euro-introduction and non-Euro currencies," LIDAM Reprints ISBA 2011052, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Francq, C. & Jiménez-Gamero, M.D. & Meintanis, S.G., 2017. "Tests for conditional ellipticity in multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 196(2), pages 305-319.
    12. Asai, Manabu, 2023. "Feasible Panel GARCH Models: Variance-Targeting Estimation and Empirical Application," Econometrics and Statistics, Elsevier, vol. 25(C), pages 23-38.

  88. Shintani, Mototsugu & Linton, Oliver, 2004. "Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos," Journal of Econometrics, Elsevier, vol. 120(1), pages 1-33, May.
    See citations under working paper version above.
  89. Xiao Z. & Linton O.B. & Carroll R.J. & Mammen E., 2003. "More Efficient Local Polynomial Estimation in Nonparametric Regression With Autocorrelated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 980-992, January.

    Cited by:

    1. Su, Liangjun, 2012. "Semiparametric GMM estimation of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 167(2), pages 543-560.
    2. David Jacho-Chavez & Arthur Lewbel & Oliver Linton, 2006. "Identification and Nonparametric Estimation of a Transformed Additively Separable Model," Boston College Working Papers in Economics 652, Boston College Department of Economics, revised 26 Nov 2008.
    3. Dabo-Niang, S. & Guillas, S. & Ternynck, C., 2016. "Efficiency in multivariate functional nonparametric models with autoregressive errors," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 168-182.
    4. Stefano Magrini & Margherita Gerolimetto, 2015. "Spatial Distribution Dynamics," ERSA conference papers ersa15p1172, European Regional Science Association.
    5. Linton, Oliver B. & Mammen, Enno, 2008. "Nonparametric transformation to white noise," Journal of Econometrics, Elsevier, vol. 142(1), pages 241-264, January.
    6. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    7. Aneiros-Perez, G. & Vilar-Fernandez, J.M., 2008. "Local polynomial estimation in partial linear regression models under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2757-2777, January.
    8. Juliane Geller & Michael H. Neumann, 2018. "Improved local polynomial estimation in time series regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-27, January.
    9. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    10. Qiong Pang & Xijian Hu, 2024. "INLA Estimation of Semi-Variable Coefficient Spatial Lag Model—Analysis of PM2.5 Influencing Factors in the Context of Urbanization in China," Mathematics, MDPI, vol. 12(7), pages 1-24, March.
    11. Alan T. K. Wan & Jinhong You & Riquan Zhang, 2016. "A Seemingly Unrelated Nonparametric Additive Model with Autoregressive Errors," Econometric Reviews, Taylor & Francis Journals, vol. 35(5), pages 894-928, May.
    12. Ozabaci, Deniz & Henderson, Daniel J., 2014. "Additive Kernel Estimates of Returns to Schooling," IZA Discussion Papers 8736, Institute of Labor Economics (IZA).
    13. Bingduo Yang & Xiaohui Liu & Liang Peng & Zongwu Cai, 2018. "Unified Tests for a Dynamic Predictive Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201808, University of Kansas, Department of Economics, revised Sep 2018.
    14. You, Jinhong & Zhou, Xian, 2006. "Statistical inference in a panel data semiparametric regression model with serially correlated errors," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 844-873, April.
    15. Martins-Filho, Carlos & Yao, Feng, 2009. "Nonparametric regression estimation with general parametric error covariance," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 309-333, March.
    16. Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
    17. Koo, Chao, 2018. "Essays on functional coefficient models," Other publications TiSEM ba87b8a5-3c55-40ec-967d-9, Tilburg University, School of Economics and Management.
    18. Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019. "Conditional Variance Forecasts for Long-Term Stock Returns," Risks, MDPI, vol. 7(4), pages 1-22, November.
    19. Degao Li & Guodong Li & Jinhong You, 2014. "Significant Variable Selection And Autoregressive Order Determination For Time-Series Partially Linear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(5), pages 478-490, August.
    20. Liu, Jun M. & Chen, Rong & Yao, Qiwei, 2010. "Nonparametric transfer function models," Journal of Econometrics, Elsevier, vol. 157(1), pages 151-164, July.
    21. Xuemei Hu & Xiaohui Liu, 2013. "Empirical likelihood confidence regions for semi-varying coefficient models with linear process errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(1), pages 161-180, March.
    22. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    23. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    24. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020. "Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    25. Margherita Gerolimetto & Stefano Magrini, 2016. "Distribution Dynamics in the US. A Spatial Perspective," Working Papers 2016:02, Department of Economics, University of Venice "Ca' Foscari".
    26. Su, Liangjun & Ullah, Aman, 2008. "Local polynomial estimation of nonparametric simultaneous equations models," Journal of Econometrics, Elsevier, vol. 144(1), pages 193-218, May.
    27. Qiu, Jia & Li, Degao & You, Jinhong, 2015. "SCAD-penalized regression for varying-coefficient models with autoregressive errors," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 100-118.
    28. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020. "Short-Term Exuberance and long-term stability: A simultaneous optimization of stock return predictions for short and long horizons," Graz Economics Papers 2020-20, University of Graz, Department of Economics.
    29. Liangjun Su & Aman Ullah & Yun Wang, 2013. "Nonparametric regression estimation with general parametric error covariance: a more efficient two-step estimator," Empirical Economics, Springer, vol. 45(2), pages 1009-1024, October.

  90. Linton, Oliver & Perron, Benoit, 2003. "The Shape of the Risk Premium: Evidence from a Semiparametric Generalized Autoregressive Conditional Heteroscedasticity Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 354-367, July.

    Cited by:

    1. Song, Zefang & Song, Xinyuan & Li, Yuan, 2023. "Bayesian Analysis of ARCH-M model with a dynamic latent variable," Econometrics and Statistics, Elsevier, vol. 28(C), pages 47-62.
    2. Seok Young Hong & Oliver Linton, 2016. "Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in?finite order," CeMMAP working papers CWP53/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Jing Li, 2021. "On Estimating Risk Premium With Flexible Fourier Form," Economics Bulletin, AccessEcon, vol. 41(3), pages 1026-1035.
    4. Hong, Seok Young & Linton, Oliver, 2020. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Journal of Econometrics, Elsevier, vol. 219(2), pages 389-424.
    5. Conrad, Christian & Mammen , Enno, 2015. "Asymptotics for parametric GARCH-in-Mean Models," Working Papers 0579, University of Heidelberg, Department of Economics.
    6. Bent Jesper Christensen & Jie Zhu & Morten Ø. Nielsen, 2009. "Long Memory In Stock Market Volatility And The Volatility-in-mean Effect: The Fiegarch-m Model," Working Paper 1207, Economics Department, Queen's University.
    7. Dias, Gustavo Fruet, 2017. "The time-varying GARCH-in-mean model," Economics Letters, Elsevier, vol. 157(C), pages 129-132.
    8. Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019. "Conditional Variance Forecasts for Long-Term Stock Returns," Risks, MDPI, vol. 7(4), pages 1-22, November.
    9. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
    10. Seok Young Hong & Oliver Linton, 2016. "Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in finite order," CeMMAP working papers 53/16, Institute for Fiscal Studies.
    11. HAFNER Christian, & KYRIAKOPOULOU Dimitra,, 2019. "Exponential-type GARCH models with linear-in-variance risk premium," LIDAM Discussion Papers CORE 2019013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. Linton, Oliver & Sancetta, Alessio, 2009. "Consistent estimation of a general nonparametric regression function in time series," Journal of Econometrics, Elsevier, vol. 152(1), pages 70-78, September.
    13. Jie Zhu, 2008. "FIEGARCH-M and and International Crises: A Cross-Country Analysis," CREATES Research Papers 2008-16, Department of Economics and Business Economics, Aarhus University.
    14. Conrad, Christian & Loch, Karin, 2012. "Anticipating Long-Term Stock Market Volatility," Working Papers 0535, University of Heidelberg, Department of Economics.

  91. Kristensen, Dennis & Linton, Oliver, 2003. "03.5.2. Consistent Standard Errors for Target Variance Approach to GARCH Estimation," Econometric Theory, Cambridge University Press, vol. 19(5), pages 879-880, October.

    Cited by:

    1. Van Dijk, Dick & Munandar, Haris & Hafner, Christian, 2011. "The Euro-introduction and non-Euro currencies," LIDAM Reprints ISBA 2011052, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  92. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function Is Not Smooth," Econometrica, Econometric Society, vol. 71(5), pages 1591-1608, September.
    See citations under working paper version above.
  93. Mototsugu Shintani & Oliver Linton, 2003. "Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(1), pages 331-357, February.
    See citations under working paper version above.
  94. Keith Vorkink & Douglas J. Hodgson & Oliver Linton, 2002. "Testing the capital asset pricing model efficiently under elliptical symmetry: a semiparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 617-639.
    See citations under working paper version above.
  95. Linton, Oliver, 2002. "Edgeworth approximations for semiparametric instrumental variable estimators and test statistics," Journal of Econometrics, Elsevier, vol. 106(2), pages 325-368, February.
    See citations under working paper version above.
  96. Zhijie Xiao & Oliver Linton, 2002. "A Nonparametric Prewhitened Covariance Estimator," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(2), pages 215-250, March.

    Cited by:

    1. Hirukawa, Masayuki, 2010. "Nonparametric multiplicative bias correction for kernel-type density estimation on the unit interval," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 473-495, February.
    2. Linton, Oliver, 2005. "Nonparametric Inference For Unbalanced Time Series Data," Econometric Theory, Cambridge University Press, vol. 21(1), pages 143-157, February.
    3. Iglesias, Emma M. & Linton, Oliver, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," UC3M Working papers. Economics we094726, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    5. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Janeway Institute Working Papers 2208, Faculty of Economics, University of Cambridge.
    6. Hirukawa, Masayuki, 2023. "Robust Covariance Matrix Estimation in Time Series: A Review," Econometrics and Statistics, Elsevier, vol. 27(C), pages 36-61.
    7. Christopher Withers & Saralees Nadarajah, 2014. "Non-parametric confidence intervals for covariance and correlation," METRON, Springer;Sapienza Università di Roma, vol. 72(3), pages 283-306, October.
    8. Park, Sujin & Hong, Seok Young & Linton, Oliver, 2016. "Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error," Journal of Econometrics, Elsevier, vol. 191(2), pages 325-347.
    9. Hirukawa Masayuki, 2004. "A Two-Stage Plug-In Bandwidth Selection and Its Implementation in Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Working Papers 04005, Concordia University, Department of Economics.
    10. Qunyong Wang & Na Wu, 2012. "Long-run covariance and its applications in cointegration regression," Stata Journal, StataCorp LP, vol. 12(3), pages 525-542, September.

  97. Arthur Lewbel & Oliver Linton, 2002. "Nonparametric Censored and Truncated Regression," Econometrica, Econometric Society, vol. 70(2), pages 765-779, March.
    See citations under working paper version above.
  98. Linton, Oliver & Whang, Yoon-Jae, 2002. "Nonparametric Estimation With Aggregated Data," Econometric Theory, Cambridge University Press, vol. 18(2), pages 420-468, April.
    See citations under working paper version above.
  99. Juan Rodríguez-Poo & Oliver Linton, 2001. "Nonparametric factor analysis of residual time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(1), pages 161-182, June.

    Cited by:

    1. Su, Liangjun & Wang, Xia, 2017. "On time-varying factor models: Estimation and testing," Journal of Econometrics, Elsevier, vol. 198(1), pages 84-101.
    2. Cristina Amado & Annastiina Silvennoinen & Timo Teräsvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," CREATES Research Papers 2018-14, Department of Economics and Business Economics, Aarhus University.
    3. Bonsoo Koo & Oliver Linton, 2013. "Let's get LADE: robust estimation of semiparametric multiplicative volatility models," CeMMAP working papers CWP11/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2020. "Time-varying general dynamic factor models and the measurement of financial connectedness," LIDAM Reprints ISBA 2020015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Hafner, Christian M. & Linton, Oliver, 2010. "Efficient estimation of a multivariate multiplicative volatility model," Journal of Econometrics, Elsevier, vol. 159(1), pages 55-73, November.
    6. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    7. Hafner, C.M. & van Dijk, D.J.C. & Franses, Ph.H.B.F., 2005. "Semi-Parametric Modelling of Correlation Dynamics," Econometric Institute Research Papers EI 2005-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Bonsoo Koo & Oliver Linton, 2013. "Let's get LADE: robust estimation of semiparametric multiplicative volatility models," CeMMAP working papers 11/13, Institute for Fiscal Studies.
    9. Aslanidis, Nektarios & Casas, Isabel, 2013. "Nonparametric correlation models for portfolio allocation," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2268-2283.

  100. Linton, Oliver, 2001. "ESTIMATING ADDITIVE NONPARAMETRIC MODELS BY PARTIAL Lq NORM: THE CURSE OF FRACTIONALITY," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1037-1050, December.
    See citations under working paper version above.
  101. Linton, Oliver & Mammen, Enno & Nielsen, Jans Perch & Tanggaard, Carsten, 2001. "Yield curve estimation by kernel smoothing methods," Journal of Econometrics, Elsevier, vol. 105(1), pages 185-223, November.
    See citations under working paper version above.
  102. Linton, Oliver & Xiao, Zhijie, 2001. "Second-Order Approximation For Adaptive Regression Estimators," Econometric Theory, Cambridge University Press, vol. 17(5), pages 984-1024, October.
    See citations under working paper version above.
  103. Gozalo, Pedro L. & Linton, Oliver B., 2001. "Testing additivity in generalized nonparametric regression models with estimated parameters," Journal of Econometrics, Elsevier, vol. 104(1), pages 1-48, August.

    Cited by:

    1. M.L. Nores & M.P. Díaz, 2016. "Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 810-826, April.
    2. Tadao Hoshino, 2014. "Quantile regression estimation of partially linear additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(3), pages 509-536, September.
    3. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers CWP20/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. David Jacho-Chavez & Arthur Lewbel & Oliver Linton, 2006. "Identification and Nonparametric Estimation of a Transformed Additively Separable Model," Boston College Working Papers in Economics 652, Boston College Department of Economics, revised 26 Nov 2008.
    5. Hall, Peter & Yatchew, Adonis, 2005. "Unified approach to testing functional hypotheses in semiparametric contexts," Journal of Econometrics, Elsevier, vol. 127(2), pages 225-252, August.
    6. Gregory Connor & Oliver Linton & Matthias Hagmann, 2007. "Efficient Estimation of a Semiparametric Characteristic-Based Factor Model of Security Returns," FMG Discussion Papers dp599, Financial Markets Group.
    7. Germán Aneiros-Pérez & Philippe Vieu, 2013. "Testing linearity in semi-parametric functional data analysis," Computational Statistics, Springer, vol. 28(2), pages 413-434, April.
    8. Zhang, Chunming & Dette, Holger, 2003. "A power comparison between nonparametric regression tests," Technical Reports 2003,22, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    9. Lawrence Dacuycuy, 2005. "On distribution approximation: a simple comparative study on procedural variations of the Zheng test," Economics Bulletin, AccessEcon, vol. 3(11), pages 1-10.
    10. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    11. Li, Qi & Hsiao, Cheng & Zinn, Joel, 2003. "Consistent specification tests for semiparametric/nonparametric models based on series estimation methods," Journal of Econometrics, Elsevier, vol. 112(2), pages 295-325, February.
    12. Joel L. Horowitz & Sokbae (Simon) Lee, 2004. "Nonparametric estimation of an additive quantile regression model," CeMMAP working papers CWP07/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Bontemps, Christophe & Simioni, Michel & Surry, Yves R., 2005. "Hedonic Housing Prices and Agricultural Pollution: An Empirical Investigation on Semiparametric Models," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24709, European Association of Agricultural Economists.
    14. Neumeyer, Natalie & Van Keilegom, Ingrid, 2010. "Estimating the error distribution in nonparametric multiple regression with applications to model testing," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1067-1078, May.
    15. Christophe Bontemps & Michel Simioni & Yves Surry, 2008. "Semiparametric hedonic price models: assessing the effects of agricultural nonpoint source pollution," Post-Print hal-02661292, HAL.
    16. Lewbel, Arthur & Lu, Xun & Su, Liangjun, 2015. "Specification testing for transformation models with an application to generalized accelerated failure-time models," Journal of Econometrics, Elsevier, vol. 184(1), pages 81-96.
    17. Zhang, Chunming & Dette, Holger, 2004. "A power comparison between nonparametric regression tests," Statistics & Probability Letters, Elsevier, vol. 66(3), pages 289-301, February.
    18. Jorge Hugo Barrientos Marín, 2006. "Estimation And Testing An Additive Partially Linear Model In A Sysmtem Of Engel Curves," Grupo Microeconomía Aplicada 034, Universidad de Antioquia, Departamento de Economía.
    19. Holger Dette & Matthias Guhlich & Natalie Neumeyer, 2015. "Testing for additivity in nonparametric quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(3), pages 437-477, June.
    20. Nitschka Thomas & Markov Nikolay, 2016. "Semi-Parametric Estimates of Taylor Rules for a Small, Open Economy – Evidence from Switzerland," German Economic Review, De Gruyter, vol. 17(4), pages 478-490, December.
    21. Joel L. Horowitz & Sokbae (Simon) Lee, 2004. "Nonparametric estimation of an additive quantile regression model," CeMMAP working papers 07/04, Institute for Fiscal Studies.
    22. Lawrence Dacuycuy, 2006. "Explaining male wage inequality in the Philippines: non-parametric and semiparametric approaches," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2497-2511.
    23. Pascal Lavergne & Valentin Patilea, 2006. "Breaking the Curse of Dimensionality in Nonparametric Testing," Working Papers 2006-24, Center for Research in Economics and Statistics.
    24. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    25. Jorge Barrientos Marín, 2005. "A note on the Bandwidth choice when the null hypothesis is semiparametric," Revista de Economía del Rosario, Universidad del Rosario, December.
    26. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers 20/12, Institute for Fiscal Studies.
    27. Gao, Jiti & King, Maxwell, 2003. "Estimation and model specification testing in nonparametric and semiparametric econometric models," MPRA Paper 11989, University Library of Munich, Germany, revised Feb 2006.
    28. Stefan Sperlich, 2014. "On the choice of regularization parameters in specification testing: a critical discussion," Empirical Economics, Springer, vol. 47(2), pages 427-450, September.
    29. Debbarh, Mohammed & Viallon, Vivian, 2008. "Testing additivity in nonparametric regression under random censorship," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2584-2591, November.
    30. Felix Abramovich & Italia Feis & Theofanis Sapatinas, 2009. "Optimal testing for additivity in multiple nonparametric regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 691-714, September.
    31. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    32. Daniel J. Henderson & Christopher F. Parmeter & Liangjun Su, 2017. "M-Estimation of a Nonparametric Threshold Regression Model," Working Papers 2017-15, University of Miami, Department of Economics.
    33. Lu, Xun & White, Halbert, 2014. "Testing for separability in structural equations," Journal of Econometrics, Elsevier, vol. 182(1), pages 14-26.

  104. Linton, Oliver B., 2000. "Efficient Estimation Of Generalized Additive Nonparametric Regression Models," Econometric Theory, Cambridge University Press, vol. 16(4), pages 502-523, August.
    See citations under working paper version above.
  105. Oliver Linton & Douglas Steigerwald, 2000. "Adaptive testing in arch models," Econometric Reviews, Taylor & Francis Journals, vol. 19(2), pages 145-174.
    See citations under working paper version above.
  106. Gozalo, Pedro & Linton, Oliver, 2000. "Local nonlinear least squares: Using parametric information in nonparametric regression," Journal of Econometrics, Elsevier, vol. 99(1), pages 63-106, November.

    Cited by:

    1. Temel, T. & Lucas, A., 2005. "Deepening the Measuring of Technical Inefficiency in Private Farming in Georgia: Locally Parametric Regression," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(1), pages 115-138.
    2. Caraveli, H. & Tsionas, E.G., 2009. "Where in the U-shaped Curve Is Europe Found? An Empirical Analysis of Centre-Periphery in the E.U," The Journal of Economic Asymmetries, Elsevier, vol. 6(1), pages 75-88.
    3. Eduardo Fé & Richard Hofler, 2013. "Count data stochastic frontier models, with an application to the patents–R&D relationship," Journal of Productivity Analysis, Springer, vol. 39(3), pages 271-284, June.
    4. Matthias HAGMANN & Olivier SCAILLET, 2003. "Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimators," FAME Research Paper Series rp91, International Center for Financial Asset Management and Engineering.
    5. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    6. Markus Froelich & Jean Bourdon & Katharina Michaelowa, 2007. "Teacher Shortages, Teacher Contracts and their Impact on Education in Africa," University of St. Gallen Department of Economics working paper series 2007 2007-20, Department of Economics, University of St. Gallen.
    7. Li, Degui & Simar, Leopold & Zelenyuk, Valentin, 2013. "To Smooth or Not to Smooth? The Case of Discrete Variables in Nonparametric Regression," LIDAM Discussion Papers ISBA 2013025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Kumbhakar, Subal & Tsionas, Efthymios, 2003. "Recent Developments in Stochastic Frontier Modeling," Efficiency Series Papers 2003/06, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    9. Tsionas, Euthimios G. & Mamatzakis, Emmanuel C., 2017. "Adjustment costs in the technical efficiency: An application to global banking," European Journal of Operational Research, Elsevier, vol. 256(2), pages 640-649.
    10. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    11. Dette, Holger & Wieczorek, Gabriele, 2007. "Testing for a constant coefficient of variation in nonparametric regression," Technical Reports 2007,36, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    12. Frölich, Markus & Michaelowa, Katharina, 2005. "Peer Effects and Textbooks in Primary Education: Evidence from Francophone Sub-Saharan Africa," IZA Discussion Papers 1519, Institute of Labor Economics (IZA).
    13. Rothe, Christoph & Firpo, Sergio, 2013. "Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions," IZA Discussion Papers 7564, Institute of Labor Economics (IZA).
    14. Mamuneas, T.P. & Savvides, A. & Stengos, T., 2002. "Economic Development and the Return to Human Capital: A Smooth Coefficient Semiparametric Approach," Working Papers 2002-14, University of Guelph, Department of Economics and Finance.
    15. Xu, Ke-Li & Phillips, Peter C. B., 2011. "Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 518-528.
    16. Puhani, Patrick & Fröhlich, Markus, 2002. "Immigration and Heterogeneous Labour in Western Germany: A Labour Market Classification Based on Nonparametric Estimation," CEPR Discussion Papers 3158, C.E.P.R. Discussion Papers.
    17. Artem Prokhorov & Kien C. Tran & Mike G. Tsionas, 2021. "Estimation of semi- and nonparametric stochastic frontier models with endogenous regressors," Empirical Economics, Springer, vol. 60(6), pages 3043-3068, June.
    18. Majda Talamakrouni & Anouar El Ghouch & Ingrid Van Keilegom, 2015. "Guided Censored Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 214-233, March.
    19. Long, Xiangdong & Su, Liangjun & Ullah, Aman, 2011. "Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 109-125.
    20. Arthur Lewbel & Daniel McFadden & Oliver Linton, 1997. "Estimating Features of a Distribution from Binomial Data," Boston College Working Papers in Economics 442, Boston College Department of Economics, revised 01 Jul 2010.
    21. Dong, Yingying & Lewbel, Arthur, 2011. "Nonparametric identification of a binary random factor in cross section data," Journal of Econometrics, Elsevier, vol. 163(2), pages 163-171, August.
    22. Li, Degui & Simar, Léopold & Zelenyuk, Valentin, 2016. "Generalized nonparametric smoothing with mixed discrete and continuous data," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 424-444.
    23. Peter C.B. Phillips & Ke-Li Xu, 2007. "Tilted Nonparametric Estimation of Volatility Functions," Cowles Foundation Discussion Papers 1612, Cowles Foundation for Research in Economics, Yale University, revised Jul 2010.
    24. Kien Tran & Efthymios Tsionas, 2010. "Local GMM Estimation of Semiparametric Panel Data with Smooth Coefficient Models," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 39-61.
    25. McCloud, Nadine & Parmeter, Christopher F., 2020. "Determining the Number of Effective Parameters in Kernel Density Estimation," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    26. Sancetta, Alessio, 2013. "Weak conditions for shrinking multivariate nonparametric density estimators," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 285-300.
    27. Talamakrouni, Majda & El Ghouch, Anouar & Van Keilegom, Ingrid, 2012. "Guided censored regression," LIDAM Discussion Papers ISBA 2012023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    28. D. F. Benoit & D. Van Den Poel, 2010. "Binary quantile regression: A Bayesian approach based on the asymmetric Laplace density," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/662, Ghent University, Faculty of Economics and Business Administration.
    29. Centorrino, Samuele & Florens, Jean-Pierre, 2021. "Nonparametric Instrumental Variable Estimation of Binary Response Models with Continuous Endogenous Regressors," Econometrics and Statistics, Elsevier, vol. 17(C), pages 35-63.
    30. Lewbel, Arthur, 2007. "A local generalized method of moments estimator," Economics Letters, Elsevier, vol. 94(1), pages 124-128, January.
    31. Yixiao Sun & Peter C.B. Phillips, 2002. "Nonlinear Log-Periodogram Regression for Perturbed Fractional Processes," Cowles Foundation Discussion Papers 1366, Cowles Foundation for Research in Economics, Yale University.
    32. Wrenn, Douglas H. & Sam, Abdoul G., 2014. "Geographically and temporally weighted likelihood regression: Exploring the spatiotemporal determinants of land use change," Regional Science and Urban Economics, Elsevier, vol. 44(C), pages 60-74.
    33. El Ghouch, Anouar & Genton, Marc G., 2009. "Local Polynomial Quantile Regression With Parametric Features," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1416-1429.
    34. Florios, Kostas & Skouras, Spyros, 2008. "Exact computation of max weighted score estimators," Journal of Econometrics, Elsevier, vol. 146(1), pages 86-91, September.

  107. Stefan Sperlich & Oliver Linton & Wolfgang Härdle, 1999. "Integration and backfitting methods in additive models-finite sample properties and comparison," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 419-458, December.
    See citations under working paper version above.
  108. Whang, Yoon-Jae & Linton, Oliver, 1999. "The asymptotic distribution of nonparametric estimates of the Lyapunov exponent for stochastic time series," Journal of Econometrics, Elsevier, vol. 91(1), pages 1-42, July.
    See citations under working paper version above.
  109. J. P. Nielsen & O. B. Linton, 1998. "An optimization interpretation of integration and back‐fitting estimators for separable nonparametric models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 217-222.
    See citations under working paper version above.
  110. Linton, Oliver, 1997. "An Asymptotic Expansion in the GARCH(l, 1) Model," Econometric Theory, Cambridge University Press, vol. 13(4), pages 558-581, February.
    See citations under working paper version above.
  111. Linton, Oliver, 1996. "Edgeworth Approximation for MINPIN Estimators in Semiparametric Regression Models," Econometric Theory, Cambridge University Press, vol. 12(1), pages 30-60, March.
    See citations under working paper version above.
  112. Linton, Oliver, 1995. "Second Order Approximation in the Partially Linear Regression Model," Econometrica, Econometric Society, vol. 63(5), pages 1079-1112, September.
    See citations under working paper version above.
  113. Linton, Oliver & Nielsen, Jens Perch, 1994. "A multiplicative bias reduction method for nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 181-187, February.

    Cited by:

    1. Xu, Ke-Li, 2010. "Reweighted Functional Estimation Of Diffusion Models," Econometric Theory, Cambridge University Press, vol. 26(2), pages 541-563, April.
    2. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
    3. Hirukawa, Masayuki, 2010. "Nonparametric multiplicative bias correction for kernel-type density estimation on the unit interval," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 473-495, February.
    4. Wenzhuan Zhang & Yingcun Xia, 2012. "Twicing local linear kernel regression smoothers," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(2), pages 399-417.
    5. Yao, Weixin, 2012. "A bias corrected nonparametric regression estimator," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 274-282.
    6. Jens Perch Nielsen & Carsten Tanggaard & M.C. Jones, 2007. "Local Linear Density Estimation for Filtered Survival Data, with Bias Correction," CREATES Research Papers 2007-13, Department of Economics and Business Economics, Aarhus University.
    7. Naito, Kanta & Yoshizaki, Masahiro, 2009. "Bandwidth selection for a data sharpening estimator in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1465-1486, August.
    8. Funke, Benedikt & Hirukawa, Masayuki, 2021. "Bias correction for local linear regression estimation using asymmetric kernels via the skewing method," Econometrics and Statistics, Elsevier, vol. 20(C), pages 109-130.
    9. Nielsen, Jens Perch & Tanggaard, Carsten & Jones, M. C., 2003. "Local Linear Density Estimation for Filtered Survival Data, with Bias Correction," Finance Working Papers 03-9, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    10. Perch Nielsen, Jens & Tanggaard, Carsten, 2000. "Boundary and Bias Correction in Kernel Hazard Estimation," Finance Working Papers 00-7, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    11. Oliver Linton, 1997. "Second Order Approximation in a Linear Regression with Heteroskedasticity for Unknown Form," Cowles Foundation Discussion Papers 1151, Cowles Foundation for Research in Economics, Yale University.
    12. Bischofberger, Stephan M. & Hiabu, Munir & Mammen, Enno & Nielsen, Jens Perch, 2019. "A comparison of in-sample forecasting methods," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 133-154.

  114. Linton, Oliver, 1993. "Adaptive Estimation in ARCH Models," Econometric Theory, Cambridge University Press, vol. 9(4), pages 539-569, August.
    See citations under working paper version above.

Chapters

  1. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

Books

  1. Linton,Oliver, 2019. "Financial Econometrics," Cambridge Books, Cambridge University Press, number 9781107177154.

    Cited by:

    1. Lajos Horváth & Hemei Li & Zhenya Liu, 2021. "How to identify the different phases of stock market bubbles statistically?," Post-Print hal-03511435, HAL.
    2. Daniel S. Hamermesh & Gerard A. Pfann, 2022. "The Variability and Volatility of Sleep: An ARCHetypal Behavior," NBER Working Papers 29658, National Bureau of Economic Research, Inc.

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