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Clifford M. Hurvich

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.

Working papers

  1. Alexander Aue & Lajos Horváth & Clifford M. Hurvich & Philippe Soulier, 2014. "Limit Laws in Transaction-Level Asset Price Models," Post-Print hal-00583372, HAL.

    Cited by:

    1. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    2. Zhang, Yichen & Hurvich, Clifford M., 2022. "Estimation of α, β and portfolio weights in a pure-jump model with long memory in volatility," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 972-994.

  2. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.

    Cited by:

    1. Meng-Chen Hsieh & Clifford Hurvich & Philippe Soulier, 2022. "Long-Horizon Return Predictability from Realized Volatility in Pure-Jump Point Processes," Papers 2202.00793, arXiv.org.
    2. Arias-Calluari, Karina & Najafi, Morteza. N. & Harré, Michael S. & Tang, Yaoyue & Alonso-Marroquin, Fernando, 2022. "Testing stationarity of the detrended price return in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    3. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    4. Zhang, Yichen & Hurvich, Clifford M., 2022. "Estimation of α, β and portfolio weights in a pure-jump model with long memory in volatility," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 972-994.

  3. Hurvich, Clifford & Wang, Yi, 2009. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration, Leverage, and Nonsynchronous Trading Effects," MPRA Paper 12575, University Library of Munich, Germany.

    Cited by:

    1. Alexander Aue & Lajos Horváth & Clifford M. Hurvich & Philippe Soulier, 2014. "Limit Laws in Transaction-Level Asset Price Models," Post-Print hal-00583372, HAL.

  4. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.

    Cited by:

    1. Kulik, Rafal & Soulier, Philippe, 2011. "The tail empirical process for long memory stochastic volatility sequences," Stochastic Processes and their Applications, Elsevier, vol. 121(1), pages 109-134, January.
    2. Kuswanto, Heri & Sibbertsen, Philipp, 2008. "A Study on "Spurious Long Memory in Nonlinear Time Series Models"," Hannover Economic Papers (HEP) dp-410, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    3. Hsieh, Meng-Chen & Hurvich, Clifford M. & Soulier, Philippe, 2007. "Asymptotics for duration-driven long range dependent processes," Journal of Econometrics, Elsevier, vol. 141(2), pages 913-949, December.

  5. Rohit Deo & Mengchen Hsieh & Clifford Hurvich, 2005. "Tracing the Source of Long Memory in Volatility," Econometrics 0501005, University Library of Munich, Germany.

    Cited by:

    1. Rohit Deo & Clifford Hurvich & Philippe Soulier & Yi Wang, 2005. "Propagation of Memory Parameter from Durations to Counts," Econometrics 0511010, University Library of Munich, Germany.
    2. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.

  6. Rohit Deo & Clifford Hurvich & Yi Lu, 2005. "Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment," Econometrics 0501002, University Library of Munich, Germany.

    Cited by:

    1. Grace Lee Ching Yap, 2020. "Optimal Filter Approximations for Latent Long Memory Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 547-568, August.
    2. Zhongjun Qu, 2010. "A Test Against Spurious Long Memory," Boston University - Department of Economics - Working Papers Series WP2010-051, Boston University - Department of Economics.
    3. Robinson Kruse & Christian Leschinski & Michael Will, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," CREATES Research Papers 2016-17, Department of Economics and Business Economics, Aarhus University.
    4. Samit Paul & Madhusudan Karmakar, 2017. "Relative Efficiency of Component GARCH-EVT Approach in Managing Intraday Market Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 21(4), pages 247-283, December.
    5. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
    6. Wen Cheong Chin & Min Cherng Lee, 2018. "S&P500 volatility analysis using high-frequency multipower variation volatility proxies," Empirical Economics, Springer, vol. 54(3), pages 1297-1318, May.
    7. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    8. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
    9. Ruiz Esther & Pérez Ana, 2012. "Maximally Autocorrelated Power Transformations: A Closer Look at the Properties of Stochastic Volatility Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-33, September.
    10. 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.
    11. Bent Jesper Christensen & Rasmus T. Varneskov, 2015. "Medium Band Least Squares Estimation of Fractional Cointegration in the Presence of Low-Frequency Contamination," CREATES Research Papers 2015-25, Department of Economics and Business Economics, Aarhus University.
    12. Zikes, Filip & Barunik, Jozef & Shenai, Nikhil, 2015. "Modeling and forecasting persistent financial durations," FinMaP-Working Papers 36, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    13. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    14. Xiang, Ju & Zhu, Xiaoneng, 2014. "Intraday asymmetric liquidity and asymmetric volatility in FTSE-100 futures market," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 134-148.
    15. Offer Lieberman & Peter Phillips, 2008. "Refined Inference on Long Memory in Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 254-267.
    16. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    17. Tobias Eckernkemper & Bastian Gribisch, 2021. "Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 883-910, August.
    18. Tim Bollerslev & Uta Kretschmer & Christian Pigorsch & George Tauchen, 2007. "A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects," CREATES Research Papers 2007-22, Department of Economics and Business Economics, Aarhus University.
    19. Alexander Aue & Lajos Horváth & Clifford M. Hurvich & Philippe Soulier, 2014. "Limit Laws in Transaction-Level Asset Price Models," Post-Print hal-00583372, HAL.
    20. Apostolos Kourtis & Raphael N. Markellos & Lazaros Symeonidis, 2016. "An International Comparison of Implied, Realized, and GARCH Volatility Forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1164-1193, December.
    21. G. Mesters & S. J. Koopman & M. Ooms, 2016. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 659-687, April.
    22. 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.
    23. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    24. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    25. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    26. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.
    27. Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, May.
    28. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    29. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    30. Yudong Wang & Zhiyuan Pan & Chongfeng Wu, 2017. "Time‐Varying Parameter Realized Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 566-580, August.
    31. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
    32. Daniel Preve & Anders Eriksson & Jun Yu, 2009. "Forecasting Realized Volatility Using A Nonnegative Semiparametric Model," Finance Working Papers 23049, East Asian Bureau of Economic Research.
    33. Jiang, George J. & Tian, Yisong S., 2010. "Forecasting Volatility Using Long Memory and Comovements: An Application to Option Valuation under SFAS 123R," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 503-533, April.
    34. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
    35. Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022. "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, vol. 28(C).
    36. Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
    37. Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
    38. Grace Yap & Wen Cheong Chin, 2016. "Spectral bandwidth selection for long memory," Modern Applied Science, Canadian Center of Science and Education, vol. 10(8), pages 1-63, August.
    39. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
    40. Thibault Vatter & Hau-Tieng Wu & Valérie Chavez-Demoulin & Bin Yu, 2015. "Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality," Econometrics, MDPI, vol. 3(4), pages 1-24, December.
    41. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2005. "Roughing it Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," NBER Working Papers 11775, National Bureau of Economic Research, Inc.
    42. Rohit Deo & Clifford Hurvich & Philippe Soulier & Yi Wang, 2005. "Propagation of Memory Parameter from Durations to Counts," Econometrics 0511010, University Library of Munich, Germany.
    43. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
    44. 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".
    45. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
    46. Andres, P. & Harvey, A., 2012. "The Dyanamic Location/Scale Model: with applications to intra-day financial data," Cambridge Working Papers in Economics 1240, Faculty of Economics, University of Cambridge.
    47. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    48. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    49. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.
    50. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
    51. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    52. J. Arteche, 2012. "Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 440-474.
    53. Pierre Perron & Wendong Shi, 2014. "Temporal Aggregation, Bandwidth Selection and Long Memory for Volatility Models," Boston University - Department of Economics - Working Papers Series wp2014-009, Boston University - Department of Economics.
    54. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    55. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    56. Alex Gonzaga & Michael Hauser, 2011. "A wavelet Whittle estimator of generalized long-memory stochastic volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 23-48, March.
    57. Christian T. Brownlees & Giampiero M. Gallo, 2010. "Comparison of Volatility Measures: a Risk Management Perspective," Journal of Financial Econometrics, Oxford University Press, vol. 8(1), pages 29-56, Winter.
    58. Zhang, Hanyu & Dufour, Alfonso, 2019. "Modeling intraday volatility of European bond markets: A data filtering application," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 131-146.
    59. Macaro, Christian, 2010. "Bayesian non-parametric signal extraction for Gaussian time series," Journal of Econometrics, Elsevier, vol. 157(2), pages 381-395, August.
    60. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.
    61. Rebecca J. Sela & Clifford M. Hurvich, 2009. "Computationally efficient methods for two multivariate fractionally integrated models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 631-651, November.
    62. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.
    63. Clements, A.E. & Hurn, A.S. & Volkov, V.V., 2016. "Common trends in global volatility," Journal of International Money and Finance, Elsevier, vol. 67(C), pages 194-214.
    64. Ferraz, Rosemeire O. & Hotta, Luiz K., 2007. "Quasi-Maximum Likelihood Estimation of Long-Memory Stochastic Volatility Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 27(2), November.
    65. Jian Zhou, 2017. "Forecasting REIT volatility with high-frequency data: a comparison of alternative methods," Applied Economics, Taylor & Francis Journals, vol. 49(26), pages 2590-2605, June.
    66. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
    67. Dumitru, Ana-Maria & Hizmeri, Rodrigo & Izzeldin, Marwan, 2019. "Forecasting the Realized Variance in the Presence of Intraday Periodicity," EconStor Preprints 193631, ZBW - Leibniz Information Centre for Economics.
    68. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
    69. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    70. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  7. Rohit Deo & Clifford Hurvich & Philippe Soulier & Yi Wang, 2005. "Propagation of Memory Parameter from Durations to Counts," Econometrics 0511010, University Library of Munich, Germany.

    Cited by:

    1. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
    2. Hurvich, Clifford & Wang, Yi, 2009. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration, Leverage, and Nonsynchronous Trading Effects," MPRA Paper 12575, University Library of Munich, Germany.

  8. Willa Chen & Clifford Hurvich, 2004. "Semiparametric Estimation of Fractional Cointegrating Subspaces," Econometrics 0412007, University Library of Munich, Germany.

    Cited by:

    1. Nielsen, Morten Orregaard & Shimotsu, Katsumi, 2007. "Determining the cointegrating rank in nonstationary fractional systems by the exact local Whittle approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 574-596, December.
    2. Katarzyna Lasak, 2008. "Likelihood based testing for no fractional cointegration," CREATES Research Papers 2008-52, Department of Economics and Business Economics, Aarhus University.
    3. Morten Ørregaard Nielsen, 2009. "Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders," CREATES Research Papers 2009-02, Department of Economics and Business Economics, Aarhus University.
    4. Hualde, J. & Robinson, P.M., 2010. "Semiparametric inference in multivariate fractionally cointegrated systems," Journal of Econometrics, Elsevier, vol. 157(2), pages 492-511, August.
    5. Hurvich, Clifford & Wang, Yi, 2009. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration, Leverage, and Nonsynchronous Trading Effects," MPRA Paper 12575, University Library of Munich, Germany.
    6. Katarzyna Lasak, 2008. "Maximum likelihood estimation of fractionally cointegrated systems," CREATES Research Papers 2008-53, Department of Economics and Business Economics, Aarhus University.
    7. Morten Ø. Nielsen & Per Houmann Frederiksen, 2008. "Fully Modified Narrow-band Least Squares Estimation Of Stationary Fractional Cointegration," Working Paper 1171, Economics Department, Queen's University.
    8. Javier Hualde, 2012. "Estimation of the cointegrating rank in fractional cointegration," Documentos de Trabajo - Lan Gaiak Departamento de Economía - Universidad Pública de Navarra 1205, Departamento de Economía - Universidad Pública de Navarra.
    9. Avarucci, M. & Velasco, C., 2008. "A wald test for the cointegration rank in nonstationary fractional systems," Research Memorandum 049, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    10. Gil-Alana, L.A., 2008. "Testing of seasonal integration and cointegration with fractionally integrated techniques: An application to the Danish labour demand," Economic Modelling, Elsevier, vol. 25(2), pages 326-339, March.

  9. Yakov Amihud & Clifford Hurvich, 2004. "Predictive Regressions: A Reduced-Bias Estimation Method," Econometrics 0412008, University Library of Munich, Germany.

    Cited by:

    1. Andrei S. Gonçalves, 2021. "Reinvestment Risk and the Equity Term Structure," Journal of Finance, American Finance Association, vol. 76(5), pages 2153-2197, October.
    2. Li, Yuan & Ran, Jimmy, 2020. "Investor Sentiment and Stock Price Premium Validation with Siamese Twins from China," Journal of Multinational Financial Management, Elsevier, vol. 57.
    3. Tim Bollerslev & Michael Gibson & Hao Zhou, 2007. "Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities," CREATES Research Papers 2007-16, Department of Economics and Business Economics, Aarhus University.
    4. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    5. Amihud, Yakov & Hurvich, Clifford M. & Wang, Yi, 2010. "Predictive regression with order-p autoregressive predictors," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 513-525, June.
    6. Ben-Rephael, Azi & Kandel, Shmuel & Wohl, Avi, 2012. "Measuring investor sentiment with mutual fund flows," Journal of Financial Economics, Elsevier, vol. 104(2), pages 363-382.
    7. Yang, Bingduo & Long, Wei & Yang, Zihui, 2022. "Testing predictability of stock returns under possible bubbles," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 246-260.
    8. Møller, Stig V. & Rangvid, Jesper, 2015. "End-of-the-year economic growth and time-varying expected returns," Journal of Financial Economics, Elsevier, vol. 115(1), pages 136-154.
    9. Ren, Yu & Tu, Yundong & Yi, Yanping, 2019. "Balanced predictive regressions," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 118-142.
    10. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    11. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    12. Robin Greenwood & Samuel G. Hanson, 2010. "Issuer Quality and Corporate Bond Returns," Harvard Business School Working Papers 11-065, Harvard Business School.
    13. Ferson, Wayne E. & Sarkissian, Sergei & Simin, Timothy, 2008. "Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(2), pages 331-353, June.
    14. Robin Greenwood & Samuel Hanson, 2013. "Waves in Ship Prices and Investment," NBER Working Papers 19246, National Bureau of Economic Research, Inc.
    15. Nieto, Belén & Rubio, Gonzalo, 2011. "The volatility of consumption-based stochastic discount factors and economic cycles," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2197-2216, September.
    16. Rahman, Md. Lutfur & Lee, Doowon & Shamsuddin, Abul, 2017. "Time-varying return predictability in South Asian equity markets," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 179-200.
    17. Ayadi, Mohamed A. & Chaibi, Anis & Kryzanowski, Lawrence, 2016. "Performance of Canadian hybrid mutual funds," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 124-147.
    18. Erik Hjalmarsson, 2008. "Predicting global stock returns," International Finance Discussion Papers 933, Board of Governors of the Federal Reserve System (U.S.).
    19. Ilaria Piatti & Fabio Trojani, 2020. "Dividend Growth Predictability and the Price–Dividend Ratio," Management Science, INFORMS, vol. 66(1), pages 130-158, January.
    20. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2016. "Predictability Hidden by Anomalous Observations," Papers 1612.05072, arXiv.org.
    21. Meng-Chen Hsieh & Clifford Hurvich & Philippe Soulier, 2022. "Long-Horizon Return Predictability from Realized Volatility in Pure-Jump Point Processes," Papers 2202.00793, arXiv.org.
    22. Davide Pettenuzzo & Allan G. Timmermann & Rossen I. Valkanov, 2008. "Return Predictability under Equilibrium Constraints on the Equity Premium," Working Papers 37, Brandeis University, Department of Economics and International Business School.
    23. Michael Johannes & Arthur Korteweg & Nicholas Polson, 2014. "Sequential Learning, Predictability, and Optimal Portfolio Returns," Journal of Finance, American Finance Association, vol. 69(2), pages 611-644, April.
    24. Menachem Brenner & Yehuda Izhakian, 2011. "Asset Priving and Ambiguity: Empirical Evidence," Working Papers 11-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    25. Bali, Turan G., 2008. "The intertemporal relation between expected returns and risk," Journal of Financial Economics, Elsevier, vol. 87(1), pages 101-131, January.
    26. Jessica A. Wachter & Missaka Warusawitharana, 2006. "Predictable returns and asset allocation: Should a skeptical investor time the market?," 2006 Meeting Papers 22, Society for Economic Dynamics.
    27. Malcolm Baker & Robin Greenwood & Jeffrey Wurgler, 2009. "Catering through Nominal Share Prices," Journal of Finance, American Finance Association, vol. 64(6), pages 2559-2590, December.
    28. Engel, Charles & Kazakova, Katya & Wang, Mengqi & Xiang, Nan, 2022. "A reconsideration of the failure of uncovered interest parity for the U.S. dollar," Journal of International Economics, Elsevier, vol. 136(C).
    29. Jin-Chuan Duan & Weiqi Zhang, 2014. "Forward-Looking Market Risk Premium," Management Science, INFORMS, vol. 60(2), pages 521-538, February.
    30. Paulo M.M. Rodrigues & Antonio Rubia, 2011. "A Class of Robust Tests in Augmented Predictive Regressions," Working Papers w201126, Banco de Portugal, Economics and Research Department.
    31. Harvey, David I & Leybourne, Stephen J & Taylor, AM Robert, 2021. "Simple Tests for Stock Return Predictability with Good Size and Power Properties," Essex Finance Centre Working Papers 29814, University of Essex, Essex Business School.
    32. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
    33. Brennan, Michael J. & Xia, Yihong, 2005. "tay's as good as cay," Finance Research Letters, Elsevier, vol. 2(1), pages 1-14, March.
    34. Han, Xing & Li, Youwei, 2017. "Can investor sentiment be a momentum time-series predictor? Evidence from China," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 212-239.
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  10. Yakov Amihud & Clifford Hurvich & Yi Wang, 2004. "Hypothesis Testing in Predictive Regressions," Finance 0412022, University Library of Munich, Germany.

    Cited by:

    1. Møller, Stig V. & Rangvid, Jesper, 2015. "End-of-the-year economic growth and time-varying expected returns," Journal of Financial Economics, Elsevier, vol. 115(1), pages 136-154.
    2. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    3. Jacob Boudoukh & Matthew Richardson & Robert Whitelaw, 2005. "The Myth of Long-Horizon Predictability," NBER Working Papers 11841, National Bureau of Economic Research, Inc.
    4. Katsumi Shimotsu & Alex Maynard, 2004. "Covariance-based orthogonality tests for regressors with unknown persistence," Econometric Society 2004 North American Summer Meetings 536, Econometric Society.

  11. Mengchen Hsieh & Clifford Hurvich & Philippe Soulier, 2004. "Asymptotics for Duration-Driven Long Range Dependent Processes," Econometrics 0412009, University Library of Munich, Germany.

    Cited by:

    1. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    2. James Davidson & Philipp Sibbertsen, 2008. "Tests of Bias in Log-Periodogram Regression," Discussion Papers 0805, University of Exeter, Department of Economics.
    3. Leipus, Remigijus & Paulauskas, Vygantas & Surgailis, Donatas, 2005. "Renewal regime switching and stable limit laws," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 299-327.
    4. Leschinski, Christian & Sibbertsen, Philipp, 2018. "The Periodogram of Spurious Long-Memory Processes," Hannover Economic Papers (HEP) dp-632, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

  12. Clifford Hurvich & Eric Moulines & Philippe Soulier, 2004. "Estimating Long Memory in Volatility," Econometrics 0412006, University Library of Munich, Germany.

    Cited by:

    1. Per Frederiksen & Morten Orregaard Nielsen, 2008. "Bias-Reduced Estimation of Long-Memory Stochastic Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 496-512, Fall.
    2. Ho, Hwai-Chung, 2015. "Sample quantile analysis for long-memory stochastic volatility models," Journal of Econometrics, Elsevier, vol. 189(2), pages 360-370.
    3. Bauwens, Luc & Chevillon, Guillaume & Laurent, Sébastien, 2023. "We modeled long memory with just one lag!," Journal of Econometrics, Elsevier, vol. 236(1).
    4. da Silva, Afonso Gonçalves & Robinson, Peter M., 2008. "Fractional Cointegration In Stochastic Volatility Models," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1207-1253, October.
    5. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    6. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
    7. Rosenbaum, Mathieu, 2008. "Estimation of the volatility persistence in a discretely observed diffusion model," Stochastic Processes and their Applications, Elsevier, vol. 118(8), pages 1434-1462, August.
    8. Arteche, Josu & Orbe, Jesus, 2016. "A bootstrap approximation for the distribution of the Local Whittle estimator," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 645-660.
    9. Arteche González, Jesús María, 2005. "Semiparametric estimation in perturbed long memory series," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    10. Bent Jesper Christensen & Rasmus T. Varneskov, 2015. "Medium Band Least Squares Estimation of Fractional Cointegration in the Presence of Low-Frequency Contamination," CREATES Research Papers 2015-25, Department of Economics and Business Economics, Aarhus University.
    11. Violetta Dalla & Liudas Giraitis & Javier Hidalgo, 2006. "Consistent estimation of the memory parameterfor nonlinear time series," STICERD - Econometrics Paper Series 497, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    12. Shao, Xiaofeng & Wu, Wei Biao, 2007. "Local asymptotic powers of nonparametric and semiparametric tests for fractional integration," Stochastic Processes and their Applications, Elsevier, vol. 117(2), pages 251-261, February.
    13. Peter M Robinson, 2007. "Multiple Local Whittle Estimation in StationarySystems," STICERD - Econometrics Paper Series 525, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    14. Arteche, Josu & Orbe, Jesus, 2009. "Using the bootstrap for finite sample confidence intervals of the log periodogram regression," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1940-1953, April.
    15. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    16. Hansen, Peter R. & Lunde, Asger, 2014. "Estimating The Persistence And The Autocorrelation Function Of A Time Series That Is Measured With Error," Econometric Theory, Cambridge University Press, vol. 30(1), pages 60-93, February.
    17. Maria Kalli & Jim Griffin, 2015. "Flexible Modeling of Dependence in Volatility Processes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 102-113, January.
    18. Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
    19. Zhenjie Liang & Futian Weng & Yuanting Ma & Yan Xu & Miao Zhu & Cai Yang, 2022. "Measurement and Analysis of High Frequency Assert Volatility Based on Functional Data Analysis," Mathematics, MDPI, vol. 10(7), pages 1-11, April.
    20. Mielniczuk, J. & Wojdyllo, P., 2007. "Estimation of Hurst exponent revisited," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4510-4525, May.
    21. Jensen Mark J., 2016. "Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 455-475, September.
    22. Pierre Perron & Zhongjun Qu, 2008. "Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices," Boston University - Department of Economics - Working Papers Series wp2008-004, Boston University - Department of Economics.
    23. Rama Cont & Purba Das, 2022. "Rough volatility: fact or artefact?," Papers 2203.13820, arXiv.org, revised Jul 2023.
    24. Gilles de Truchis & Elena Ivona Dumitrescu, 2019. "Narrow-band Weighted Nonlinear Least Squares Estimation of Unbalanced Cointegration Systems," EconomiX Working Papers 2019-14, University of Paris Nanterre, EconomiX.
    25. Marie Busch & Philipp Sibbertsen, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Econometrics, MDPI, vol. 6(1), pages 1-21, March.
    26. Afonso Gonçalves da Silva & Peter M Robinson, 2006. "Finite Sample Performance in CointegrationAnalysis of Nonlinear Time Series with LongMemory," STICERD - Econometrics Paper Series 501, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    27. Rasmus T. Varneskov & Pierre Perron, 2017. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series WP2017-006, Boston University - Department of Economics.
    28. Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, May.
    29. David W. Berger & Alain P. Chaboud & Erik Hjalmarsson & Edward Howorka, 2006. "What drives volatility persistence in the foreign exchange market?," International Finance Discussion Papers 862, Board of Governors of the Federal Reserve System (U.S.).
    30. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    31. Dalla, Violetta & Giraitis, Liudas & Hidalgo, Javier, 2006. "Consistent estimation of the memory parameter for nonlinear time series," LSE Research Online Documents on Economics 6813, London School of Economics and Political Science, LSE Library.
    32. Gabriele La Spada & Fabrizio Lillo, 2011. "The effect of round-off error on long memory processes," Papers 1107.4476, arXiv.org, revised Mar 2013.
    33. Gilles de Truchis & Elena Ivona Dumitrescu, 2019. "Narrow-band Weighted Nonlinear Least Squares Estimation of Unbalanced Cointegration Systems," Working Papers hal-04141871, HAL.
    34. Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
    35. Eduardo Rossi & Paolo Santucci de Magistris, 2012. "Estimation of long memory in integrated variance," DEM Working Papers Series 017, University of Pavia, Department of Economics and Management.
    36. Jonathan H. Wright, 2000. "Log-periodogram estimation of long memory volatility dependencies with conditionally heavy tailed returns," International Finance Discussion Papers 685, Board of Governors of the Federal Reserve System (U.S.).
    37. Per Frederiksen & Frank S. Nielsen, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-59, Department of Economics and Business Economics, Aarhus University.
    38. Gilles de Truchis & Florent Dubois & Elena Ivona Dumitrescu, 2019. "Local Whittle Analysis of Stationary Unbalanced Fractional Cointegration Systems," Working Papers hal-04141882, HAL.
    39. García-Enríquez, Javier & Hualde, Javier, 2019. "Local Whittle estimation of long memory: Standard versus bias-reducing techniques," Econometrics and Statistics, Elsevier, vol. 12(C), pages 66-77.
    40. Gilles de Truchis & Elena Ivona Dumitrescu & Florent Dubois, 2019. "Local Whittle Analysis of Stationary Unbalanced Fractional Cointegration Systems," EconomiX Working Papers 2019-15, University of Paris Nanterre, EconomiX.
    41. Stefano Grassi & Paolo Santucci de Magistris, 2011. "When Long Memory Meets the Kalman Filter: A Comparative Study," CREATES Research Papers 2011-14, Department of Economics and Business Economics, Aarhus University.
    42. Rohit Deo & Clifford Hurvich & Philippe Soulier & Yi Wang, 2005. "Propagation of Memory Parameter from Durations to Counts," Econometrics 0511010, University Library of Munich, Germany.
    43. Michael Levine & Soledad Torres & Frederi Viens, 2009. "Estimators for the long-memory parameter in LARCH models, and fractional Brownian motion," Statistical Inference for Stochastic Processes, Springer, vol. 12(3), pages 221-250, October.
    44. Mathieu Rosenbaum, 2006. "Estimation of the Volatility Persistence in a Discretly Observed Diffusion Model," Working Papers 2006-02, Center for Research in Economics and Statistics.
    45. Josu Arteche, 2012. "Standard and seasonal long memory in volatility: an application to Spanish inflation," Empirical Economics, Springer, vol. 42(3), pages 693-712, June.
    46. Artiach, Miguel & Arteche, Josu, 2012. "Doubly fractional models for dynamic heteroscedastic cycles," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2139-2158.
    47. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2017. "The Memory of Stock Return Volatility: Asset Pricing Implications," Hannover Economic Papers (HEP) dp-613, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    48. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    49. Hou, Jie & Perron, Pierre, 2014. "Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations," Journal of Econometrics, Elsevier, vol. 182(2), pages 309-328.
    50. Ting Zhang & Hwai-Chung Ho & Martin Wendler & Wei Biao Wu, 2013. "Block Sampling under Strong Dependence," Papers 1312.5807, arXiv.org.
    51. K. Nadarajah & Gael M. Martin & D.S. Poskitt, 2014. "Issues in the Estimation of Mis-Specified Models of Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 18/14, Monash University, Department of Econometrics and Business Statistics.
    52. Daniela Osterrieder & Daniel Ventosa-Santaulària & J. Eduardo Vera-Valdés, 2015. "Unbalanced Regressions and the Predictive Equation," CREATES Research Papers 2015-09, Department of Economics and Business Economics, Aarhus University.
    53. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.
    54. Lopes, Sílvia R.C. & Prass, Taiane S., 2014. "Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 278-307.
    55. Becker, Janis & Hollstein, Fabian & Prokopczuk, Marcel & Sibbertsen, Philipp, 2019. "The Memory of Beta Factors," Hannover Economic Papers (HEP) dp-661, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    56. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
    57. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    58. J. Arteche, 2012. "Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 440-474.
    59. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print hal-00815563, HAL.
    60. Frank S. Nielsen, 2008. "Local polynomial Whittle estimation covering non-stationary fractional processes," CREATES Research Papers 2008-28, Department of Economics and Business Economics, Aarhus University.
    61. Robinson, Peter M., 2007. "Multiple local whittle estimation in stationary systems," LSE Research Online Documents on Economics 4436, London School of Economics and Political Science, LSE Library.
    62. Josu Arteche & Javier García‐Enríquez, 2022. "Singular spectrum analysis for value at risk in stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 3-16, January.
    63. Frederiksen, Per & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2012. "Local polynomial Whittle estimation of perturbed fractional processes," Journal of Econometrics, Elsevier, vol. 167(2), pages 426-447.
    64. Tian, Shuairu & Hamori, Shigeyuki, 2016. "Time-varying price shock transmission and volatility spillover in foreign exchange, bond, equity, and commodity markets: Evidence from the United States," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 163-171.
    65. Gilles Truchis & Benjamin Keddad, 2016. "Long-Run Comovements in East Asian Stock Market Volatility," Open Economies Review, Springer, vol. 27(5), pages 969-986, November.
    66. Masaki Narukawa & Yasumasa Matsuda, 2008. "Broadband semiparametric estimation of the long-memory parameter by the likelihood-based FEXP approach," TERG Discussion Papers 239, Graduate School of Economics and Management, Tohoku University.
    67. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2013. "Indirect Inference in fractional short-term interest rate diffusions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 109-126.

Articles

  1. Kovtun, Vladimir & Giloni, Avi & Hurvich, Clifford, 2019. "The value of sharing disaggregated information in supply chains," European Journal of Operational Research, Elsevier, vol. 277(2), pages 469-478.

    Cited by:

    1. Vladimir Kovtun & Avi Giloni & Clifford Hurvich & Sridhar Seshadri, 2023. "Pivot Clustering to Minimize Error in Forecasting Aggregated Demand Streams Each Following an Autoregressive Moving Average Model," Stats, MDPI, vol. 6(4), pages 1-28, November.
    2. Weidong Zhang & Fuqiang Wang, 2022. "Information Sharing in Competing Supply Chains with Carbon Emissions Reduction Incentives," Sustainability, MDPI, vol. 14(20), pages 1-25, October.
    3. Jiali Wang & Xue Peng & Yunan Du & Fulin Wang, 2022. "A tripartite evolutionary game research on information sharing of the subjects of agricultural product supply chain with a farmer cooperative as the core enterprise," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(1), pages 159-177, January.
    4. Lu, Jizhou & Feng, Gengzhong & Shum, Stephen & Lai, Kin Keung, 2021. "On the value of information sharing in the presence of information errors," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1139-1152.

  2. Pierre Perron & Eduardo Zorita & Wen Cao & Clifford Hurvich & Philippe Soulier, 2017. "Drift in Transaction-Level Asset Price Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 769-790, September.
    See citations under working paper version above.
  3. Aue, Alexander & Horváth, Lajos & Hurvich, Clifford & Soulier, Philippe, 2014. "Limit Laws In Transaction-Level Asset Price Models," Econometric Theory, Cambridge University Press, vol. 30(3), pages 536-579, June.
    See citations under working paper version above.
  4. Vladimir Kovtun & Avi Giloni & Clifford Hurvich, 2014. "Assessing the value of demand sharing in supply chains," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(7), pages 515-531, October.

    Cited by:

    1. Lei Li & Ting Chi & Tongtong Hao & Tao Yu, 2018. "Customer demand analysis of the electronic commerce supply chain using Big Data," Annals of Operations Research, Springer, vol. 268(1), pages 113-128, September.
    2. Kovtun, Vladimir & Giloni, Avi & Hurvich, Clifford, 2019. "The value of sharing disaggregated information in supply chains," European Journal of Operational Research, Elsevier, vol. 277(2), pages 469-478.
    3. Lu, Jizhou & Feng, Gengzhong & Shum, Stephen & Lai, Kin Keung, 2021. "On the value of information sharing in the presence of information errors," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1139-1152.

  5. Avi Giloni & Clifford Hurvich & Sridhar Seshadri, 2014. "Forecasting and information sharing in supply chains under ARMA demand," IISE Transactions, Taylor & Francis Journals, vol. 46(1), pages 35-54.

    Cited by:

    1. Ketzenberg, Michael & Oliva, Rogelio & Wang, Yimin & Webster, Scott, 2023. "Retailer inventory data sharing in a fresh product supply chain," European Journal of Operational Research, Elsevier, vol. 307(2), pages 680-693.
    2. Vladimir Kovtun & Avi Giloni & Clifford Hurvich, 2014. "Assessing the value of demand sharing in supply chains," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(7), pages 515-531, October.
    3. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    4. Vladimir Kovtun & Avi Giloni & Clifford Hurvich & Sridhar Seshadri, 2023. "Pivot Clustering to Minimize Error in Forecasting Aggregated Demand Streams Each Following an Autoregressive Moving Average Model," Stats, MDPI, vol. 6(4), pages 1-28, November.
    5. Ruomeng Cui & Gad Allon & Achal Bassamboo & Jan A. Van Mieghem, 2015. "Information Sharing in Supply Chains: An Empirical and Theoretical Valuation," Management Science, INFORMS, vol. 61(11), pages 2803-2824, November.
    6. Kovtun, Vladimir & Giloni, Avi & Hurvich, Clifford, 2019. "The value of sharing disaggregated information in supply chains," European Journal of Operational Research, Elsevier, vol. 277(2), pages 469-478.
    7. Lu, Jizhou & Feng, Gengzhong & Shum, Stephen & Lai, Kin Keung, 2021. "On the value of information sharing in the presence of information errors," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1139-1152.

  6. Cheryl J. Flynn & Clifford M. Hurvich & Jeffrey S. Simonoff, 2013. "Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1031-1043, September.

    Cited by:

    1. Hollstein, Fabian & Prokopczuk, Marcel & Tharann, Björn & Wese Simen, Chardin, 2021. "Predictability in commodity markets: Evidence from more than a century," Journal of Commodity Markets, Elsevier, vol. 24(C).
    2. Haili Zhang & Guohua Zou, 2020. "Cross-Validation Model Averaging for Generalized Functional Linear Model," Econometrics, MDPI, vol. 8(1), pages 1-35, February.
    3. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    4. Zeebari, Zangin & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2021. "Regularized Conditional Estimators of Unit Inefficiency in Stochastic Frontier Analysis, with Application to Electricity Distribution Market," Ratio Working Papers 345, The Ratio Institute.
    5. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    6. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    7. Xinyu Zhang & Dalei Yu & Guohua Zou & Hua Liang, 2016. "Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1775-1790, October.
    8. Chen, Xingyi & Li, Haiqi & Zhang, Jing, 2023. "Complete subset averaging approach for high-dimensional generalized linear models," Economics Letters, Elsevier, vol. 226(C).
    9. Shangwei Zhao & Jun Liao & Dalei Yu, 2020. "Model averaging estimator in ridge regression and its large sample properties," Statistical Papers, Springer, vol. 61(4), pages 1719-1739, August.
    10. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    12. 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.

  7. Rebecca J. Sela & Clifford M. Hurvich, 2012. "The averaged periodogram estimator for a power law in coherency," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(2), pages 340-363, March.

    Cited by:

    1. Dong, Keqiang & Zhang, Hong & Gao, You, 2017. "Dynamical mechanism in aero-engine gas path system using minimum spanning tree and detrended cross-correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 363-369.
    2. Li, Ming, 2017. "Record length requirement of long-range dependent teletraffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 164-187.
    3. Kakinaka, Shinji & Umeno, Ken, 2021. "Exploring asymmetric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    4. Kristoufek, Ladislav, 2015. "On the interplay between short and long term memory in the power-law cross-correlations setting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 218-222.
    5. Todea, Alexandru, 2016. "Cross-correlations between volatility, volatility persistence and stock market integration: the case of emergent stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 87(C), pages 208-215.
    6. Kristoufek, Ladislav, 2013. "Mixed-correlated ARFIMA processes for power-law cross-correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6484-6493.
    7. Kristoufek, Ladislav, 2015. "Can the bivariate Hurst exponent be higher than an average of the separate Hurst exponents?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 431(C), pages 124-127.
    8. Ladislav Kristoufek, 2016. "Power-law cross-correlations estimation under heavy tails," Papers 1602.05385, arXiv.org, revised Apr 2016.
    9. Kristoufek, Ladislav, 2014. "Measuring correlations between non-stationary series with DCCA coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 291-298.
    10. Ladislav Kristoufek, 2014. "Spectrum-based estimators of the bivariate Hurst exponent," Papers 1408.6637, arXiv.org, revised Nov 2014.
    11. Ladislav Kristoufek, 2018. "Power-law cross-correlations: Issues, solutions and future challenges," Papers 1806.01616, arXiv.org.
    12. Shen, Chenhua, 2019. "The influence of a scaling exponent on ρDCCA: A spatial cross-correlation pattern of precipitation records over eastern China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 579-590.
    13. Qin, Jing & Ge, Jintian & Lu, Xinsheng, 2018. "The effectiveness of the monetary policy in China: New evidence from long-range cross-correlation analysis and the components of multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1026-1037.
    14. Abry, Patrice & Didier, Gustavo, 2018. "Wavelet eigenvalue regression for n-variate operator fractional Brownian motion," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 75-104.
    15. Patrice Abry & Gustavo Didier & Hui Li, 2019. "Two-step wavelet-based estimation for Gaussian mixed fractional processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(2), pages 157-185, July.
    16. da Silva, Marcus Fernandes & de Area Leão Pereira, Éder Johnson & da Silva Filho, Aloisio Machado & de Castro, Arleys Pereira Nunes & Miranda, José Garcia Vivas & Zebende, Gilney Figueira, 2016. "Quantifying the contagion effect of the 2008 financial crisis between the G7 countries (by GDP nominal)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 1-8.
    17. Fernández-Martínez, M. & Sánchez-Granero, M.A. & Casado Belmonte, M.P. & Trinidad Segovia, J.E., 2020. "A note on power-law cross-correlated processes," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    18. Ladislav Kristoufek, 2013. "Testing power-law cross-correlations: Rescaled covariance test," Papers 1307.4727, arXiv.org, revised Aug 2013.
    19. Kristoufek, Ladislav, 2015. "Finite sample properties of power-law cross-correlations estimators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 513-525.
    20. Sophie Achard & Irène Gannaz, 2016. "Multivariate Wavelet Whittle Estimation in Long-range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(4), pages 476-512, July.
    21. Machado Filho, A. & da Silva, M.F. & Zebende, G.F., 2014. "Autocorrelation and cross-correlation in time series of homicide and attempted homicide," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 12-19.

  8. Hurvich, Clifford M. & Wang, Yi, 2010. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 539-558.

    Cited by:

    1. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    2. Alexander Aue & Lajos Horváth & Clifford M. Hurvich & Philippe Soulier, 2014. "Limit Laws in Transaction-Level Asset Price Models," Post-Print hal-00583372, HAL.
    3. Zhang, Yichen & Hurvich, Clifford M., 2022. "Estimation of α, β and portfolio weights in a pure-jump model with long memory in volatility," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 972-994.

  9. Amihud, Yakov & Hurvich, Clifford M. & Wang, Yi, 2010. "Predictive regression with order-p autoregressive predictors," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 513-525, June.

    Cited by:

    1. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    2. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2016. "Predictability Hidden by Anomalous Observations," Papers 1612.05072, arXiv.org.
    3. Kim, Jae H., 2014. "Testing for parameter restrictions in a stationary VAR model: A bootstrap alternative," Economic Modelling, Elsevier, vol. 41(C), pages 267-273.
    4. Amélie Charles & Olivier Darné & Jae H. Kim, 2016. "Stock Return Predictability: Evaluation based on prediction intervals," Working Papers hal-01295037, HAL.
    5. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Jayetileke, Harshanie L. & Wang, You-Gan & Zhu, Min, 2021. "Predictive regression with p-lags and order-q autoregressive predictors," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 282-293.
    7. Paulo M.M. Rodrigues & Matei Demetrescu, 2016. "Residual-augmented IVX predictive regression," Working Papers w201605, Banco de Portugal, Economics and Research Department.
    8. Tom Engsted & Thomas Q. Pedersen, 2008. "Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model," CREATES Research Papers 2008-27, Department of Economics and Business Economics, Aarhus University.
    9. Jacob Boudoukh & Ronen Israel & Matthew P. Richardson, 2020. "Biases in Long-Horizon Predictive Regressions," NBER Working Papers 27410, National Bureau of Economic Research, Inc.
    10. Jurdi, Doureige & Kim, Jae, 2019. "Predicting the U.S. Stock Market Return: Evidence from the Improved Augmented Regression Method," MPRA Paper 94028, University Library of Munich, Germany.
    11. Eiji Kurozumi & Kohei Aono, 2011. "Estimation and Inference in Predictive Regressions," Global COE Hi-Stat Discussion Paper Series gd11-192, Institute of Economic Research, Hitotsubashi University.
    12. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
    13. Kim, Jae H. & Shamsuddin, Abul, 2020. "A bootstrap test for predictability of asset returns," Finance Research Letters, Elsevier, vol. 35(C).
    14. Amélie Charles & Olivier Darné & Jae H Kim, 2017. "International Stock Return Predictability: Evidence from New Statistical Tests," Post-Print hal-01626101, HAL.
    15. Oktay Ozkan, 2020. "Time-varying return predictability and adaptive markets hypothesis: Evidence on MIST countries from a novel wild bootstrap likelihood ratio approach," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 34(2), pages 101-113.
    16. Peter C.B. Phillips & Ye Chen, "undated". "Restricted Likelihood Ratio Tests in Predictive Regression," Cowles Foundation Discussion Papers 1968, Cowles Foundation for Research in Economics, Yale University.
    17. Jurdi, Doureige J., 2022. "Predicting the Australian equity risk premium," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).

  10. Deo, Rohit & Hurvich, Clifford M. & Soulier, Philippe & Wang, Yi, 2009. "Conditions For The Propagation Of Memory Parameter From Durations To Counts And Realized Volatility," Econometric Theory, Cambridge University Press, vol. 25(3), pages 764-792, June.

    Cited by:

    1. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    2. Meng-Chen Hsieh & Clifford Hurvich & Philippe Soulier, 2022. "Long-Horizon Return Predictability from Realized Volatility in Pure-Jump Point Processes," Papers 2202.00793, arXiv.org.
    3. Zikes, Filip & Barunik, Jozef & Shenai, Nikhil, 2015. "Modeling and forecasting persistent financial durations," FinMaP-Working Papers 36, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    4. A E Clements & A S Hurn & K A Lindsay & V Volkov, 2023. "Estimating a Non-parametric Memory Kernel for Mutually Exciting Point Processes," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1759-1790.
    5. Mawuli Segnon & Manuel Stapper, 2019. "Long Memory Conditional Heteroscedasticity in Count Data," CQE Working Papers 8219, Center for Quantitative Economics (CQE), University of Muenster.
    6. Lavancier, Frédéric & Philippe, Anne & Surgailis, Donatas, 2010. "A two-sample test for comparison of long memory parameters," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2118-2136, October.
    7. Aldrich, Eric M. & Heckenbach, Indra & Laughlin, Gregory, 2016. "A compound duration model for high-frequency asset returns," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 105-128.
    8. Jan Beran & Yuanhua Feng & Sucharita Ghosh, 2015. "Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models," Statistical Papers, Springer, vol. 56(2), pages 431-451, May.
    9. Zhang, Yichen & Hurvich, Clifford M., 2022. "Estimation of α, β and portfolio weights in a pure-jump model with long memory in volatility," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 972-994.

  11. Yakov Amihud & Clifford M. Hurvich & Yi Wang, 2009. "Multiple-Predictor Regressions: Hypothesis Testing," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 413-434, January.

    Cited by:

    1. Li, Yuan & Ran, Jimmy, 2020. "Investor Sentiment and Stock Price Premium Validation with Siamese Twins from China," Journal of Multinational Financial Management, Elsevier, vol. 57.
    2. 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.
    3. Møller, Stig V. & Rangvid, Jesper, 2015. "End-of-the-year economic growth and time-varying expected returns," Journal of Financial Economics, Elsevier, vol. 115(1), pages 136-154.
    4. Ren, Yu & Tu, Yundong & Yi, Yanping, 2019. "Balanced predictive regressions," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 118-142.
    5. Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Ilaria Piatti & Fabio Trojani, 2020. "Dividend Growth Predictability and the Price–Dividend Ratio," Management Science, INFORMS, vol. 66(1), pages 130-158, January.
    7. Robin Greenwood & Samuel G. Hanson & Andrei Shleifer & Jakob Ahm Sørensen, 2022. "Predictable Financial Crises," Journal of Finance, American Finance Association, vol. 77(2), pages 863-921, April.
    8. Paulo M.M. Rodrigues & Antonio Rubia, 2011. "A Class of Robust Tests in Augmented Predictive Regressions," Working Papers w201126, Banco de Portugal, Economics and Research Department.
    9. Harvey, David I & Leybourne, Stephen J & Taylor, AM Robert, 2021. "Simple Tests for Stock Return Predictability with Good Size and Power Properties," Essex Finance Centre Working Papers 29814, University of Essex, Essex Business School.
    10. Nuno Silva, 2013. "Equity Premia Predictability in the EuroZone," GEMF Working Papers 2013-22, GEMF, Faculty of Economics, University of Coimbra.
    11. Han, Xing & Li, Youwei, 2017. "Can investor sentiment be a momentum time-series predictor? Evidence from China," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 212-239.
    12. Zuzana Rakovska, 2020. "Composite Survey Sentiment as a Predictor of Future Market Returns: Evidence for German Equity Indices," Working Papers 2020/13, Czech National Bank.
    13. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    14. Sirio Aramonte & Mohammad Jahan-Parvar & Justin Shugarman, 2015. "Institutions and return predictability in oil-exporting countries," Finance and Economics Discussion Series 2015-14, Board of Governors of the Federal Reserve System (U.S.).
    15. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    16. Tom Engsted & Thomas Q. Pedersen, 2011. "Bias-correction in vector autoregressive models: A simulation study," CREATES Research Papers 2011-18, Department of Economics and Business Economics, Aarhus University.
    17. Marshall, Ben R. & Nguyen, Hung T. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2021. "Country governance and international equity returns," Journal of Banking & Finance, Elsevier, vol. 122(C).
    18. Paulo M.M. Rodrigues & Matei Demetrescu, 2016. "Residual-augmented IVX predictive regression," Working Papers w201605, Banco de Portugal, Economics and Research Department.
    19. Tom Engsted & Thomas Q. Pedersen, 2008. "Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model," CREATES Research Papers 2008-27, Department of Economics and Business Economics, Aarhus University.
    20. Ferson, Wayne & Nallareddy, Suresh & Xie, Biqin, 2013. "The “out-of-sample” performance of long run risk models," Journal of Financial Economics, Elsevier, vol. 107(3), pages 537-556.
    21. Stig V. Møller & Jesper Rangvid, 2012. "End-of-the-year economic growth and time-varying expected returns," CREATES Research Papers 2012-42, Department of Economics and Business Economics, Aarhus University.
    22. 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.
    23. Fukang Zhu & Zongwu Cai & Liang Peng, 2014. "Predictive regressions for macroeconomic data," Papers 1404.7642, arXiv.org.
    24. Schrimpf, Andreas, 2008. "International Stock Return Predictability Under Model Uncertainty," ZEW Discussion Papers 08-048, ZEW - Leibniz Centre for European Economic Research.
    25. Jagjeev Dosanjh, 2017. "Exchange Initiatives and Market Efficiency: Evidence from the Australian Securities Exchange," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2017.
    26. Stefan Bruder, 2014. "Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions," ECON - Working Papers 181, Department of Economics - University of Zurich, revised Dec 2015.
    27. Stefan Nagel & Zhengyang Xu, 2022. "Dynamics of Subjective Risk Premia," NBER Working Papers 29803, National Bureau of Economic Research, Inc.
    28. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    29. Bakshi, Gurdip & Panayotov, George & Skoulakis, Georgios, 2011. "Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios," Journal of Financial Economics, Elsevier, vol. 100(3), pages 475-495, June.
    30. Cai, Zongwu & Wang, Yunfei, 2014. "Testing predictive regression models with nonstationary regressors," Journal of Econometrics, Elsevier, vol. 178(P1), pages 4-14.
    31. Amélie Charles & Olivier Darné & Jae H Kim, 2017. "International Stock Return Predictability: Evidence from New Statistical Tests," Post-Print hal-01626101, HAL.
    32. Gungor, Sermin & Luger, Richard, 2020. "Small-sample tests for stock return predictability with possibly non-stationary regressors and GARCH-type effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 750-770.
    33. Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.
    34. Wenjie Ding & Khelifa Mazouz & Qingwei Wang, 2019. "Investor sentiment and the cross-section of stock returns: new theory and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 53(2), pages 493-525, August.
    35. Guo, Hui & Qiu, Buhui, 2014. "Options-implied variance and future stock returns," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 93-113.
    36. Wayne E. Ferson & Suresh K. Nallareddy & Biqin Xie, 2012. "The "Out of Sample" Performance of Long-run Risk Models," NBER Working Papers 17848, National Bureau of Economic Research, Inc.
    37. Trojani, Fabio & Wiehenkamp, Christian & Wrampelmeyer, Jan, 2014. "Ambiguity and Reality," Working Papers on Finance 1418, University of St. Gallen, School of Finance.
    38. Bakshi, Gurdip & Panayotov, George, 2013. "Predictability of currency carry trades and asset pricing implications," Journal of Financial Economics, Elsevier, vol. 110(1), pages 139-163.
    39. Joni Kokkonen & Matti Suominen, 2015. "Hedge Funds and Stock Market Efficiency," Management Science, INFORMS, vol. 61(12), pages 2890-2904, December.
    40. Samuel M. Hartzmark, 2016. "Economic Uncertainty and Interest Rates," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 6(2), pages 179-220.
    41. Liu, Xiaohui & Yang, Bingduo & Cai, Zongwu & Peng, Liang, 2019. "A unified test for predictability of asset returns regardless of properties of predicting variables," Journal of Econometrics, Elsevier, vol. 208(1), pages 141-159.

  12. Rebecca J. Sela & Clifford M. Hurvich, 2009. "Computationally efficient methods for two multivariate fractionally integrated models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 631-651, November.

    Cited by:

    1. Hung Do & Rabindra Nepal & Tooraj Jamasb, 2020. "Electricity market integration, decarbonisation and security of supply: Dynamic volatility connectedness in the Irish and Great Britain markets," CAMA Working Papers 2020-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Gbaguidi DAVID, 2011. "Expectations Impact On The Effectiveness Of The Inflation-Real Activity Trade-Off," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 2(2), pages 141-181.
    3. Sibbertsen, Philipp & Leschinski, Christian & Busch, Marie, 2018. "A multivariate test against spurious long memory," Journal of Econometrics, Elsevier, vol. 203(1), pages 33-49.
    4. Kristoufek, Ladislav, 2015. "On the interplay between short and long term memory in the power-law cross-correlations setting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 218-222.
    5. Paulo M.M. Rodrigues & Marina Balboa, 2021. "Multivariate Fractional Integration Tests allowing for Conditional Heteroskedasticity with an Application to Return Volatility and Trading Volume," Working Papers w202102, Banco de Portugal, Economics and Research Department.
    6. Yao Axel Ehouman, 2020. "Volatility transmission between oil prices and banks’ stock prices as a new source of instability: Lessons from the United States experience," Post-Print hal-02960571, HAL.
    7. G. Mesters & S. J. Koopman & M. Ooms, 2016. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 659-687, April.
    8. Kristoufek, Ladislav, 2013. "Mixed-correlated ARFIMA processes for power-law cross-correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6484-6493.
    9. Stefanos Kechagias & Vladas Pipiras, 2020. "Modeling bivariate long‐range dependence with general phase," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 268-292, March.
    10. Yao Axel Ehouman, 2019. "Volatility transmission between oil prices and banks stock prices as a new source of instability: Lessons from the US Experience," EconomiX Working Papers 2019-19, University of Paris Nanterre, EconomiX.
    11. Ehouman, Yao Axel, 2020. "Volatility transmission between oil prices and banks' stock prices as a new source of instability: Lessons from the United States experience," Economic Modelling, Elsevier, vol. 91(C), pages 198-217.
    12. Ladislav Kristoufek, 2018. "Power-law cross-correlations: Issues, solutions and future challenges," Papers 1806.01616, arXiv.org.
    13. Gbaguidi, David, 2012. "La courbe de Phillips : temps d’arbitrage et/ou arbitrage de temps," L'Actualité Economique, Société Canadienne de Science Economique, vol. 88(1), pages 87-119, mars.
    14. Yao Axel Ehouman, 2019. "Volatility transmission between oil prices and banks stock prices as a new source of instability: Lessons from the US Experience," Working Papers hal-04141868, HAL.
    15. Ross Doppelt & Keith O'Hara, 2018. "Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks," 2018 Meeting Papers 1212, Society for Economic Dynamics.
    16. Do, Hung Xuan & Brooks, Robert & Treepongkaruna, Sirimon & Wu, Eliza, 2016. "Stock and currency market linkages: New evidence from realized spillovers in higher moments," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 167-185.
    17. Bensalma, Ahmed, 2018. "Two Distinct Seasonally Fractionally Differenced Periodic Processes," MPRA Paper 84969, University Library of Munich, Germany.

  13. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2008. "Corrigendum to "Estimating Long Memory in Volatility"," Econometrica, Econometric Society, vol. 76(3), pages 661-662, May.

    Cited by:

    1. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.
    2. J. Arteche, 2012. "Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 440-474.

  14. Hsieh, Meng-Chen & Hurvich, Clifford M. & Soulier, Philippe, 2007. "Asymptotics for duration-driven long range dependent processes," Journal of Econometrics, Elsevier, vol. 141(2), pages 913-949, December.
    See citations under working paper version above.
  15. Deo, Rohit & Hurvich, Clifford & Lu, Yi, 2006. "Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 29-58.
    See citations under working paper version above.
  16. Chen, Willa W. & Hurvich, Clifford M. & Lu, Yi, 2006. "On the Correlation Matrix of the Discrete Fourier Transform and the Fast Solution of Large Toeplitz Systems for Long-Memory Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 812-822, June.

    Cited by:

    1. Pai, Jeffrey & Ravishanker, Nalini, 2009. "A multivariate preconditioned conjugate gradient approach for maximum likelihood estimation in vector long memory processes," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1282-1289, May.
    2. Andreas Noack Jensen & Morten Ørregaard Nielsen, 2014. "A Fast Fractional Difference Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(5), pages 428-436, August.
    3. Zevallos, Mauricio & Palma, Wilfredo, 2013. "Minimum distance estimation of ARFIMA processes," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 242-256.
    4. Pai, Jeffrey & Ravishanker, Nalini, 2015. "Fast approximate likelihood evaluation for stable VARFIMA processes," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 160-168.
    5. Rohit Deo & Mengchen Hsieh & Clifford Hurvich, 2005. "Tracing the Source of Long Memory in Volatility," Econometrics 0501005, University Library of Munich, Germany.
    6. Rohit Deo & Clifford Hurvich & Yi Lu, 2005. "Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment," Econometrics 0501002, University Library of Munich, Germany.
    7. McLeod, A. Ian & Yu, Hao & Krougly, Zinovi L., 2007. "Algorithms for Linear Time Series Analysis: With R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i05).
    8. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
    9. Rebecca J. Sela & Clifford M. Hurvich, 2009. "Computationally efficient methods for two multivariate fractionally integrated models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 631-651, November.

  17. Hurvich, Clifford & Lang, Gabriel & Soulier, Philippe, 2005. "Estimation of Long Memory in the Presence of a Smooth Nonparametric Trend," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 853-871, September.

    Cited by:

    1. Zhongjun Qu, 2010. "A Test Against Spurious Long Memory," Boston University - Department of Economics - Working Papers Series WP2010-051, Boston University - Department of Economics.
    2. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
    3. Fabrizio Iacone, 2010. "Local Whittle estimation of the memory parameter in presence of deterministic components," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(1), pages 37-49, January.
    4. Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
    5. Linyuan Li & Kewei Lu, 2013. "On rate-optimal nonparametric wavelet regression with long memory moving average errors," Statistical Inference for Stochastic Processes, Springer, vol. 16(2), pages 127-145, July.
    6. Dalla, Violetta & Giraitis, Liudas & Robinson, Peter M., 2020. "Asymptotic theory for time series with changing mean and variance," Journal of Econometrics, Elsevier, vol. 219(2), pages 281-313.

  18. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2005. "Estimating Long Memory in Volatility," Econometrica, Econometric Society, vol. 73(4), pages 1283-1328, July.
    See citations under working paper version above.
  19. Amihud, Yakov & Hurvich, Clifford M., 2004. "Predictive Regressions: A Reduced-Bias Estimation Method," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 813-841, December.
    See citations under working paper version above.
  20. Chen, Willa W. & Hurvich, Clifford M., 2003. "Estimating fractional cointegration in the presence of polynomial trends," Journal of Econometrics, Elsevier, vol. 117(1), pages 95-121, November.

    Cited by:

    1. Nielsen, Morten Orregaard & Shimotsu, Katsumi, 2007. "Determining the cointegrating rank in nonstationary fractional systems by the exact local Whittle approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 574-596, December.
    2. Benjamin M. Tabak, 2007. "Estimating the Fractional Order of Integration of Yields in the Brazilian Fixed Income Market," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 36(3), pages 231-246, November.
    3. Iacone, Fabrizio & Robinson, Peter M., 2004. "Cointegration in fractional systems with deterministic trends," LSE Research Online Documents on Economics 2232, London School of Economics and Political Science, LSE Library.
    4. Javier Hualde & Fabrizio Iacone, 2015. "Small-b and Fixed-b Asymptotics for Weighted Covariance Estimation in Fractional Cointegration," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(4), pages 528-540, July.
    5. Samet Gunay, 2018. "Fractionally Cointegrated Vector Autoregression Model: Evaluation of High/Low and Close/Open Spreads for Precious Metals," SAGE Open, , vol. 8(4), pages 21582440188, November.
    6. Javier Hualde & Morten Ørregaard Nielsen, 2022. "Truncated sum-of-squares estimation of fractional time series models with generalized power law trend," Working Paper 1458, Economics Department, Queen's University.
    7. Müller, Ulrich K. & Watson, Mark W., 2013. "Low-frequency robust cointegration testing," Journal of Econometrics, Elsevier, vol. 174(2), pages 66-81.
    8. Hualde Javier & Iacone Fabrizio, 2012. "First Stage Estimation of Fractional Cointegration," Journal of Time Series Econometrics, De Gruyter, vol. 4(1), pages 1-32, May.
    9. Willa Chen & Clifford Hurvich, 2004. "Semiparametric Estimation of Fractional Cointegrating Subspaces," Econometrics 0412007, University Library of Munich, Germany.
    10. Javier Hualde & Fabrizio Iacone, 2015. "Autocorrelation robust inference using the Daniell kernel with fixed bandwidth," Discussion Papers 15/14, Department of Economics, University of York.
    11. Bent Jesper Christensen & Morten Ø. Nielsen, "undated". "Semiparametric Analysis of Stationary Fractional Cointegration and the Implied-Realized Volatility Relation in High-Frequency Options Data," Economics Working Papers 2001-4, Department of Economics and Business Economics, Aarhus University.
    12. Hurvich, Clifford & Wang, Yi, 2009. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration, Leverage, and Nonsynchronous Trading Effects," MPRA Paper 12575, University Library of Munich, Germany.
    13. Hsieh, Meng-Chen & Hurvich, Clifford M. & Soulier, Philippe, 2007. "Asymptotics for duration-driven long range dependent processes," Journal of Econometrics, Elsevier, vol. 141(2), pages 913-949, December.
    14. Katarzyna Lasak, 2008. "Maximum likelihood estimation of fractionally cointegrated systems," CREATES Research Papers 2008-53, Department of Economics and Business Economics, Aarhus University.
    15. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
    16. Morten Ø. Nielsen & Per Houmann Frederiksen, 2008. "Fully Modified Narrow-band Least Squares Estimation Of Stationary Fractional Cointegration," Working Paper 1171, Economics Department, Queen's University.
    17. Daniela Osterrieder & Peter C. Schotman, 2012. "The Volatility of Long-term Bond Returns: Persistent Interest Shocks and Time-varying Risk Premiums," CREATES Research Papers 2012-35, Department of Economics and Business Economics, Aarhus University.
    18. Hualde, Javier & Iacone, Fabrizio, 2017. "Fixed bandwidth asymptotics for the studentized mean of fractionally integrated processes," Economics Letters, Elsevier, vol. 150(C), pages 39-43.
    19. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.

  21. Clifford M. Hurvich & Bonnie K. Ray, 2003. "The Local Whittle Estimator of Long-Memory Stochastic Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 1(3), pages 445-470.

    Cited by:

    1. Per Frederiksen & Morten Orregaard Nielsen, 2008. "Bias-Reduced Estimation of Long-Memory Stochastic Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 496-512, Fall.
    2. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    3. Rosenbaum, Mathieu, 2008. "Estimation of the volatility persistence in a discretely observed diffusion model," Stochastic Processes and their Applications, Elsevier, vol. 118(8), pages 1434-1462, August.
    4. Arteche González, Jesús María, 2005. "Semiparametric estimation in perturbed long memory series," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    5. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    6. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    7. Mielniczuk, J. & Wojdyllo, P., 2007. "Estimation of Hurst exponent revisited," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4510-4525, May.
    8. Jensen Mark J., 2016. "Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 455-475, September.
    9. Marie Busch & Philipp Sibbertsen, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Econometrics, MDPI, vol. 6(1), pages 1-21, March.
    10. Afonso Gonçalves da Silva & Peter M Robinson, 2006. "Finite Sample Performance in CointegrationAnalysis of Nonlinear Time Series with LongMemory," STICERD - Econometrics Paper Series 501, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    11. David W. Berger & Alain P. Chaboud & Erik Hjalmarsson & Edward Howorka, 2006. "What drives volatility persistence in the foreign exchange market?," International Finance Discussion Papers 862, Board of Governors of the Federal Reserve System (U.S.).
    12. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2005. "Estimating Long Memory in Volatility," Econometrica, Econometric Society, vol. 73(4), pages 1283-1328, July.
    13. Gabriele La Spada & Fabrizio Lillo, 2011. "The effect of round-off error on long memory processes," Papers 1107.4476, arXiv.org, revised Mar 2013.
    14. Eduardo Rossi & Paolo Santucci de Magistris, 2012. "Estimation of long memory in integrated variance," DEM Working Papers Series 017, University of Pavia, Department of Economics and Management.
    15. Proietti, Tommaso, 2014. "Exponential Smoothing, Long Memory and Volatility Prediction," MPRA Paper 57230, University Library of Munich, Germany.
    16. Per Frederiksen & Frank S. Nielsen, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-59, Department of Economics and Business Economics, Aarhus University.
    17. García-Enríquez, Javier & Hualde, Javier, 2019. "Local Whittle estimation of long memory: Standard versus bias-reducing techniques," Econometrics and Statistics, Elsevier, vol. 12(C), pages 66-77.
    18. Stefano Grassi & Paolo Santucci de Magistris, 2011. "When Long Memory Meets the Kalman Filter: A Comparative Study," CREATES Research Papers 2011-14, Department of Economics and Business Economics, Aarhus University.
    19. Mathieu Rosenbaum, 2006. "Estimation of the Volatility Persistence in a Discretly Observed Diffusion Model," Working Papers 2006-02, Center for Research in Economics and Statistics.
    20. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2017. "The Memory of Stock Return Volatility: Asset Pricing Implications," Hannover Economic Papers (HEP) dp-613, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    21. Hou, Jie & Perron, Pierre, 2014. "Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations," Journal of Econometrics, Elsevier, vol. 182(2), pages 309-328.
    22. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    23. K. Nadarajah & Gael M. Martin & D.S. Poskitt, 2014. "Issues in the Estimation of Mis-Specified Models of Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 18/14, Monash University, Department of Econometrics and Business Statistics.
    24. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.
    25. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
    26. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    27. J. Arteche, 2012. "Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 440-474.
    28. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print hal-00815563, HAL.
    29. Frank S. Nielsen, 2008. "Local polynomial Whittle estimation covering non-stationary fractional processes," CREATES Research Papers 2008-28, Department of Economics and Business Economics, Aarhus University.
    30. Alex Gonzaga & Michael Hauser, 2011. "A wavelet Whittle estimator of generalized long-memory stochastic volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 23-48, March.
    31. Frederiksen, Per & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2012. "Local polynomial Whittle estimation of perturbed fractional processes," Journal of Econometrics, Elsevier, vol. 167(2), pages 426-447.
    32. Ewa M. Syczewska, 2010. "Financial crisis influence on the BUX index of Hungarian stock exchange. Long memory measures: 1991-2008," Working Papers 46, Department of Applied Econometrics, Warsaw School of Economics.
    33. Huang, Teng-Ching & Tu, Yu-Chen & Chou, Heng-Chih, 2015. "Long memory and the relation between options and stock prices," Finance Research Letters, Elsevier, vol. 12(C), pages 77-91.

  22. Chen, Willa W. & Hurvich, Clifford M., 2003. "Semiparametric Estimation of Multivariate Fractional Cointegration," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 629-642, January.

    Cited by:

    1. Christian Leschinski & Michelle Voges & Philipp Sibbertsen, 2021. "Integration and Disintegration of EMU Government Bond Markets," Econometrics, MDPI, vol. 9(1), pages 1-17, March.
    2. Nielsen, Morten Orregaard & Shimotsu, Katsumi, 2007. "Determining the cointegrating rank in nonstationary fractional systems by the exact local Whittle approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 574-596, December.
    3. Federico Carlini & Paolo Santucci de Magistris, 2019. "On the Identification of Fractionally Cointegrated VAR Models With the Condition," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 134-146, January.
    4. Rodrigues, Paulo M.M. & Sibbertsen, Philipp & Voges, Michelle, 2019. "Testing for breaks in the cointegrating relationship: On the stability of government bond markets' equilibrium," Hannover Economic Papers (HEP) dp-656, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    5. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    6. Stefanos Kechagias & Vladas Pipiras, 2015. "Definitions And Representations Of Multivariate Long-Range Dependent Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 1-25, January.
    7. Christian Leschinski & Michelle Voges & Philipp Sibbertsen, 2021. "A comparison of semiparametric tests for fractional cointegration," Statistical Papers, Springer, vol. 62(4), pages 1997-2030, August.
    8. Müller, Ulrich K. & Watson, Mark W., 2013. "Low-frequency robust cointegration testing," Journal of Econometrics, Elsevier, vol. 174(2), pages 66-81.
    9. Hualde Javier & Iacone Fabrizio, 2012. "First Stage Estimation of Fractional Cointegration," Journal of Time Series Econometrics, De Gruyter, vol. 4(1), pages 1-32, May.
    10. Willa Chen & Clifford Hurvich, 2004. "Semiparametric Estimation of Fractional Cointegrating Subspaces," Econometrics 0412007, University Library of Munich, Germany.
    11. Katarzyna Lasak & Carlos Velasco, 2013. "Fractional cointegration rank estimation," CREATES Research Papers 2013-08, Department of Economics and Business Economics, Aarhus University.
    12. Morten Ørregaard Nielsen, 2009. "Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders," CREATES Research Papers 2009-02, Department of Economics and Business Economics, Aarhus University.
    13. Claudio Morana & Fabio Cesare Bagliano, 2007. "Inflation and monetary dynamics in the USA: a quantity-theory approach," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 229-244.
    14. Hualde, J. & Robinson, P.M., 2010. "Semiparametric inference in multivariate fractionally cointegrated systems," Journal of Econometrics, Elsevier, vol. 157(2), pages 492-511, August.
    15. Bent Jesper Christensen & Morten Ø. Nielsen, "undated". "Semiparametric Analysis of Stationary Fractional Cointegration and the Implied-Realized Volatility Relation in High-Frequency Options Data," Economics Working Papers 2001-4, Department of Economics and Business Economics, Aarhus University.
    16. Hurvich, Clifford & Wang, Yi, 2009. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration, Leverage, and Nonsynchronous Trading Effects," MPRA Paper 12575, University Library of Munich, Germany.
    17. Hsieh, Meng-Chen & Hurvich, Clifford M. & Soulier, Philippe, 2007. "Asymptotics for duration-driven long range dependent processes," Journal of Econometrics, Elsevier, vol. 141(2), pages 913-949, December.
    18. Katarzyna Lasak, 2008. "Maximum likelihood estimation of fractionally cointegrated systems," CREATES Research Papers 2008-53, Department of Economics and Business Economics, Aarhus University.
    19. Avarucci, Marco & Marinucci, Domenico, 2005. "Polynomial cointegration among stationary processes with long memory," UC3M Working papers. Economics we055123, Universidad Carlos III de Madrid. Departamento de Economía.
    20. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
    21. Morten Ø. Nielsen & Per Houmann Frederiksen, 2008. "Fully Modified Narrow-band Least Squares Estimation Of Stationary Fractional Cointegration," Working Paper 1171, Economics Department, Queen's University.
    22. Dechert, Andreas, 2014. "Fraktionale Kointegrationsbeziehungen zwischen Euribor-Zinssätzen," W.E.P. - Würzburg Economic Papers 93, University of Würzburg, Department of Economics.
    23. Avarucci, M. & Velasco, C., 2008. "A wald test for the cointegration rank in nonstationary fractional systems," Research Memorandum 049, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

  23. Hurvich, Clifford M. & Soulier, Philippe, 2002. "Testing For Long Memory In Volatility," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1291-1308, December.

    Cited by:

    1. Rosenbaum, Mathieu, 2008. "Estimation of the volatility persistence in a discretely observed diffusion model," Stochastic Processes and their Applications, Elsevier, vol. 118(8), pages 1434-1462, August.
    2. Shao, Xiaofeng & Wu, Wei Biao, 2007. "Local asymptotic powers of nonparametric and semiparametric tests for fractional integration," Stochastic Processes and their Applications, Elsevier, vol. 117(2), pages 251-261, February.
    3. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2005. "Estimating Long Memory in Volatility," Econometrica, Econometric Society, vol. 73(4), pages 1283-1328, July.
    4. Duffy, Ken & King, Christopher & Malone, David, 2007. "Ambiguities in estimates of critical exponents for long-range dependent processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 43-52.
    5. Luis G. Gorostiza & Reyla A. Navarro & Eliane R. Rodrigues, 2004. "Some Long-Range Dependence Processes Arising from Fluctuations of Particle Systems," RePAd Working Paper Series lrsp-TRS401, Département des sciences administratives, UQO.
    6. Mathieu Rosenbaum, 2006. "Estimation of the Volatility Persistence in a Discretly Observed Diffusion Model," Working Papers 2006-02, Center for Research in Economics and Statistics.
    7. Karanasos, M. & Kartsaklas, A., 2009. "Dual long-memory, structural breaks and the link between turnover and the range-based volatility," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 838-851, December.
    8. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
    9. Hsieh, Meng-Chen & Hurvich, Clifford M. & Soulier, Philippe, 2007. "Asymptotics for duration-driven long range dependent processes," Journal of Econometrics, Elsevier, vol. 141(2), pages 913-949, December.
    10. J. Arteche, 2012. "Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 440-474.

  24. Hurvich, Clifford M. & Moulines, Eric & Soulier, Philippe, 2002. "The FEXP estimator for potentially non-stationary linear time series," Stochastic Processes and their Applications, Elsevier, vol. 97(2), pages 307-340, February.

    Cited by:

    1. Willa Chen & Clifford Hurvich, 2004. "Semiparametric Estimation of Fractional Cointegrating Subspaces," Econometrics 0412007, University Library of Munich, Germany.
    2. Yixiao Sun, 2005. "Adaptive Estimation of the Regression Discontinuity Model," Econometrics 0506003, University Library of Munich, Germany.
    3. Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
    4. Hsieh, Meng-Chen & Hurvich, Clifford M. & Soulier, Philippe, 2007. "Asymptotics for duration-driven long range dependent processes," Journal of Econometrics, Elsevier, vol. 141(2), pages 913-949, December.
    5. Jan Beran & Sucharita Ghosh, 2020. "Estimating the Mean Direction of Strongly Dependent Circular Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 210-228, March.
    6. Chen, Willa W. & Hurvich, Clifford M., 2003. "Estimating fractional cointegration in the presence of polynomial trends," Journal of Econometrics, Elsevier, vol. 117(1), pages 95-121, November.

  25. Hurvich, Clifford M., 2002. "Multistep forecasting of long memory series using fractional exponential models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 167-179.

    Cited by:

    1. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    2. Man, K. S., 2003. "Long memory time series and short term forecasts," International Journal of Forecasting, Elsevier, vol. 19(3), pages 477-491.
    3. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    4. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
    5. McElroy, Tucker & Politis, Dimitris N., 2012. "Fixed-B Asymptotics For The Studentized Mean From Time Series With Short, Long, Or Negative Memory," Econometric Theory, Cambridge University Press, vol. 28(2), pages 471-481, April.
    6. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    7. Tommaso Proietti & Niels Haldrup & Oskar Knapik, 2017. "Spikes and memory in (Nord Pool) electricity price spot prices," CREATES Research Papers 2017-39, Department of Economics and Business Economics, Aarhus University.
    8. McElroy, Tucker S. & Holan, Scott H., 2016. "Computation of the autocovariances for time series with multiple long-range persistencies," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 44-56.
    9. McElroy, Tucker S. & Politis, Dimitris N., 2012. "Distribution Theory for the Studentized Mean for Long, Short, and Negative Memory Time Series," University of California at San Diego, Economics Working Paper Series qt35c7r55c, Department of Economics, UC San Diego.
    10. Proietti, Tommaso & Luati, Alessandra, 2013. "The Exponential Model for the Spectrum of a Time Series: Extensions and Applications," MPRA Paper 45280, University Library of Munich, Germany.
    11. Rohit Deo & Clifford Hurvich & Yi Lu, 2005. "Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment," Econometrics 0501002, University Library of Munich, Germany.
    12. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    13. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.

  26. Clifford M. Hurvich & Julia Brodsky, 2001. "Broadband Semiparametric Estimation of the Memory Parameter of a Long‐Memory Time Series Using Fractional Exponential Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(2), pages 221-249, March.

    Cited by:

    1. Kanchana Nadarajah & Gael M Martin & Donald S Poskitt, 2019. "Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach," Monash Econometrics and Business Statistics Working Papers 7/19, Monash University, Department of Econometrics and Business Statistics.
    2. Javier Hualde & Peter M Robinson, 2006. "Semiparametric Estimation of Fractional Cointegration," STICERD - Econometrics Paper Series 502, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Violetta Dalla & Liudas Giraitis & Javier Hidalgo, 2006. "Consistent estimation of the memory parameterfor nonlinear time series," STICERD - Econometrics Paper Series 497, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    4. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    5. Chen, Yen-Hung & Hsu, Nan-Jung, 2014. "A frequency domain test for detecting nonstationary time series," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 179-189.
    6. Robinson, Peter & Henry, Marc, 2002. "Higher-order kernel semiparametric M-estimation of long memory," LSE Research Online Documents on Economics 2147, London School of Economics and Political Science, LSE Library.
    7. Dalla, Violetta & Giraitis, Liudas & Hidalgo, Javier, 2006. "Consistent estimation of the memory parameter for nonlinear time series," LSE Research Online Documents on Economics 6813, London School of Economics and Political Science, LSE Library.
    8. Liudas Giraitis & Peter M Robinson, 2002. "Edgeworth Expansions for Semiparametric Whittle Estimation of Long Memory," STICERD - Econometrics Paper Series 438, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    9. Bhansali, R.J. & Giraitis, L. & Kokoszka, P.S., 2006. "Estimation of the memory parameter by fitting fractionally differenced autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2101-2130, November.
    10. Giraitis, L. & Robinson, P.M., 2003. "Edgeworth expansions for semiparametric Whittle estimation of long memory," LSE Research Online Documents on Economics 291, London School of Economics and Political Science, LSE Library.
    11. Uwe Hassler & Marc-Oliver Pohle, 2019. "Forecasting under Long Memory and Nonstationarity," Papers 1910.08202, arXiv.org.
    12. García-Enríquez, Javier & Hualde, Javier, 2019. "Local Whittle estimation of long memory: Standard versus bias-reducing techniques," Econometrics and Statistics, Elsevier, vol. 12(C), pages 66-77.
    13. Ana Pérez & Esther Ruiz, 2002. "Modelos de memoria larga para series económicas y financieras," Investigaciones Economicas, Fundación SEPI, vol. 26(3), pages 395-445, September.
    14. Hualde, J. & Robinson, P.M., 2010. "Semiparametric inference in multivariate fractionally cointegrated systems," Journal of Econometrics, Elsevier, vol. 157(2), pages 492-511, August.
    15. Hurvich, Clifford M., 2002. "Multistep forecasting of long memory series using fractional exponential models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 167-179.
    16. Hualde, Javier & Robinson, Peter M., 2006. "Semiparametric Estimation of Fractional Cointegration," LSE Research Online Documents on Economics 4537, London School of Economics and Political Science, LSE Library.
    17. Hurvich, Clifford M. & Moulines, Eric & Soulier, Philippe, 2002. "The FEXP estimator for potentially non-stationary linear time series," Stochastic Processes and their Applications, Elsevier, vol. 97(2), pages 307-340, February.
    18. Bhansali, R. J. & Kokoszka, P. S., 2002. "Computation of the forecast coefficients for multistep prediction of long-range dependent time series," International Journal of Forecasting, Elsevier, vol. 18(2), pages 181-206.
    19. Jan Beran & Sucharita Ghosh, 2020. "Estimating the Mean Direction of Strongly Dependent Circular Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 210-228, March.
    20. Giraitis, Liudas & Robinson, Peter, 2002. "Edgeworth expansions for semiparametric Whittle estimation of long memory," LSE Research Online Documents on Economics 2130, London School of Economics and Political Science, LSE Library.
    21. Masaki Narukawa, 2016. "Semiparametric Whittle estimation of a cyclical long-memory time series based on generalised exponential models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 272-295, June.
    22. Chen, Willa W. & Hurvich, Clifford M., 2003. "Estimating fractional cointegration in the presence of polynomial trends," Journal of Econometrics, Elsevier, vol. 117(1), pages 95-121, November.
    23. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    24. Masaki Narukawa & Yasumasa Matsuda, 2008. "Broadband semiparametric estimation of the long-memory parameter by the likelihood-based FEXP approach," TERG Discussion Papers 239, Graduate School of Economics and Management, Tohoku University.

  27. Deo, Rohit S. & Hurvich, Clifford M., 2001. "On The Log Periodogram Regression Estimator Of The Memory Parameter In Long Memory Stochastic Volatility Models," Econometric Theory, Cambridge University Press, vol. 17(4), pages 686-710, August.

    Cited by:

    1. Per Frederiksen & Morten Orregaard Nielsen, 2008. "Bias-Reduced Estimation of Long-Memory Stochastic Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 496-512, Fall.
    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," Center for Financial Institutions Working Papers 99-08, Wharton School Center for Financial Institutions, University of Pennsylvania.
    3. Ho, Hwai-Chung, 2015. "Sample quantile analysis for long-memory stochastic volatility models," Journal of Econometrics, Elsevier, vol. 189(2), pages 360-370.
    4. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
    5. Casas, Isabel & Gao, Jiti, 2008. "Econometric estimation in long-range dependent volatility models: Theory and practice," Journal of Econometrics, Elsevier, vol. 147(1), pages 72-83, November.
    6. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    7. Manabu Asai & Michael McAleer, 2013. "A Fractionally Integrated Wishart Stochastic Volatility Model," Tinbergen Institute Discussion Papers 13-025/III, Tinbergen Institute.
    8. Pierre Perron & Zhongjun Qu, 2007. "An Analytical Evaluation of the Log-periodogram Estimate in the Presence of Level Shifts," Boston University - Department of Economics - Working Papers Series wp2007-044, Boston University - Department of Economics.
    9. Arteche González, Jesús María, 2005. "Semiparametric estimation in perturbed long memory series," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    10. Arteche González, Jesús María, 2002. "Gaussian Semiparametric Estimation in Long Memory in Stochastic Volatility and Signal Plus Noise Models," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    11. Yang K. Lu & Pierre Perron, 2008. "Modeling and Forecasting Stock Return Volatility Using a Random Level Shift Model," Boston University - Department of Economics - Working Papers Series wp2008-012, Boston University - Department of Economics.
    12. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," Global COE Hi-Stat Discussion Paper Series gd08-032, Institute of Economic Research, Hitotsubashi University.
    13. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Consistent Inference for Predictive Regressions in Persistent Economic Systems," NBER Working Papers 28568, National Bureau of Economic Research, Inc.
    14. Violetta Dalla & Liudas Giraitis & Javier Hidalgo, 2006. "Consistent estimation of the memory parameterfor nonlinear time series," STICERD - Econometrics Paper Series 497, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    15. Smith, Aaron D., 2004. "Level Shifts and the Illusion of Long Memory in Economic Time Series," Working Papers 11974, University of California, Davis, Department of Agricultural and Resource Economics.
    16. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    17. John Cotter & Simon Stevenson, 2011. "Modeling Long Memory in REITs," Papers 1103.5414, arXiv.org.
    18. Gourieroux, Christian & Josiak, Joann, 1999. "Nonlinear persistence and copersistence," CEPREMAP Working Papers (Couverture Orange) 9920, CEPREMAP.
    19. Shelton Peiris & Manabu Asai & Michael McAleer, 2016. "Estimating and forecasting generalized fractional Long memory stochastic volatility models," Documentos de Trabajo del ICAE 2016-08, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    20. Jensen Mark J., 2016. "Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 455-475, September.
    21. Pierre Perron & Zhongjun Qu, 2008. "Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices," Boston University - Department of Economics - Working Papers Series wp2008-004, Boston University - Department of Economics.
    22. Wolfgang Härdle & Julius Mungo, 2007. "Long Memory Persistence in the Factor of Implied Volatility Dynamics," SFB 649 Discussion Papers SFB649DP2007-027, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    23. Fleming, Jeff & Kirby, Chris, 2011. "Long memory in volatility and trading volume," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1714-1726, July.
    24. Afonso Gonçalves da Silva & Peter M Robinson, 2006. "Finite Sample Performance in CointegrationAnalysis of Nonlinear Time Series with LongMemory," STICERD - Econometrics Paper Series 501, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    25. Hélène Hamisultane, 2006. "Pricing the Weather Derivatives in the Presence of Long Memory in Temperatures," Working Papers halshs-00079197, HAL.
    26. Robinson, Peter & Henry, Marc, 2002. "Higher-order kernel semiparametric M-estimation of long memory," LSE Research Online Documents on Economics 2147, London School of Economics and Political Science, LSE Library.
    27. Rasmus T. Varneskov & Pierre Perron, 2017. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series WP2017-006, Boston University - Department of Economics.
    28. Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, May.
    29. John Cotter, 2011. "Minimum Capital Requirement Calculations for UK Futures," Working Papers 200418, Geary Institute, University College Dublin.
    30. Mark J. Jensen, 2004. "Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 895-922, November.
    31. Zhongjun Qu & Pierre Perron, 2008. "A Stochastic Volatility Model with Random Level Shifts: Theory and Applications to S&P 500 and NASDAQ Return Indices," Boston University - Department of Economics - Working Papers Series wp2008-007, Boston University - Department of Economics.
    32. Willa Chen & Rohit Deo, 2005. "GMM Estimation for Long Memory Latent Variable Volatility and Duration Models," Econometrics 0501006, University Library of Munich, Germany.
    33. Dalla, Violetta & Giraitis, Liudas & Hidalgo, Javier, 2006. "Consistent estimation of the memory parameter for nonlinear time series," LSE Research Online Documents on Economics 6813, London School of Economics and Political Science, LSE Library.
    34. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2005. "Estimating Long Memory in Volatility," Econometrica, Econometric Society, vol. 73(4), pages 1283-1328, July.
    35. Jiang, George J. & Tian, Yisong S., 2010. "Forecasting Volatility Using Long Memory and Comovements: An Application to Option Valuation under SFAS 123R," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 503-533, April.
    36. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," NBER Working Papers 11188, National Bureau of Economic Research, Inc.
    37. Eduardo Rossi & Paolo Santucci de Magistris, 2012. "Estimation of long memory in integrated variance," DEM Working Papers Series 017, University of Pavia, Department of Economics and Management.
    38. Proietti, Tommaso, 2014. "Exponential Smoothing, Long Memory and Volatility Prediction," MPRA Paper 57230, University Library of Munich, Germany.
    39. Jonathan H. Wright, 2000. "Log-periodogram estimation of long memory volatility dependencies with conditionally heavy tailed returns," International Finance Discussion Papers 685, Board of Governors of the Federal Reserve System (U.S.).
    40. Per Frederiksen & Frank S. Nielsen, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-59, Department of Economics and Business Economics, Aarhus University.
    41. García-Enríquez, Javier & Hualde, Javier, 2019. "Local Whittle estimation of long memory: Standard versus bias-reducing techniques," Econometrics and Statistics, Elsevier, vol. 12(C), pages 66-77.
    42. Stefano Grassi & Paolo Santucci de Magistris, 2011. "When Long Memory Meets the Kalman Filter: A Comparative Study," CREATES Research Papers 2011-14, Department of Economics and Business Economics, Aarhus University.
    43. Rohit Deo & Clifford Hurvich & Philippe Soulier & Yi Wang, 2005. "Propagation of Memory Parameter from Durations to Counts," Econometrics 0511010, University Library of Munich, Germany.
    44. Rohit Deo & Mengchen Hsieh & Clifford Hurvich, 2005. "Tracing the Source of Long Memory in Volatility," Econometrics 0501005, University Library of Munich, Germany.
    45. Ana Pérez & Esther Ruiz, 2002. "Modelos de memoria larga para series económicas y financieras," Investigaciones Economicas, Fundación SEPI, vol. 26(3), pages 395-445, September.
    46. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    47. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    48. Hou, Jie & Perron, Pierre, 2014. "Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations," Journal of Econometrics, Elsevier, vol. 182(2), pages 309-328.
    49. John Cotter, 2004. "Realized volatility and minimum capital requirements," Money Macro and Finance (MMF) Research Group Conference 2003 20, Money Macro and Finance Research Group.
    50. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    51. K. Nadarajah & Gael M. Martin & D.S. Poskitt, 2014. "Issues in the Estimation of Mis-Specified Models of Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 18/14, Monash University, Department of Econometrics and Business Statistics.
    52. Christian Gourieroux & Joann Jasiak, 2011. "Nonlinear Persistence and Copersistence," Palgrave Macmillan Books, in: Greg N. Gregoriou & Razvan Pascalau (ed.), Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration, chapter 4, pages 77-103, Palgrave Macmillan.
    53. Casas, Isabel, 2008. "Estimation of stochastic volatility with LRD," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 335-340.
    54. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
    55. Hsieh, Meng-Chen & Hurvich, Clifford M. & Soulier, Philippe, 2007. "Asymptotics for duration-driven long range dependent processes," Journal of Econometrics, Elsevier, vol. 141(2), pages 913-949, December.
    56. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    57. J. Arteche, 2012. "Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 440-474.
    58. Souza, Leonardo Rocha, 2003. "The aliasing effect, the Fejer Kernel and temporally aggregated long memory processes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 470, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    59. Jin Lee, 2004. "Wavelet transform for log periodogram regression in long memory stochastic volatility model," Econometric Society 2004 Far Eastern Meetings 682, Econometric Society.
    60. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 613-649, December.
    61. Josu Arteche & Javier García‐Enríquez, 2022. "Singular spectrum analysis for value at risk in stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 3-16, January.
    62. Frederiksen, Per & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2012. "Local polynomial Whittle estimation of perturbed fractional processes," Journal of Econometrics, Elsevier, vol. 167(2), pages 426-447.
    63. Torben G. Andersen & Rasmus T. Varneskov, 2018. "Consistent Inference for Predictive Regressions in Persistent VAR Economies," CREATES Research Papers 2018-09, Department of Economics and Business Economics, Aarhus University.
    64. M. Shelton Peiris & Manabu Asai, 2016. "Generalized Fractional Processes with Long Memory and Time Dependent Volatility Revisited," Econometrics, MDPI, vol. 4(3), pages 1-21, September.
    65. Fajardo, Fabio Alexander, 2011. "Some Alternatives for Robust Estimation of the Spectrum in Stationary Processes," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(1), March.
    66. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    67. Maheu John, 2005. "Can GARCH Models Capture Long-Range Dependence?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-43, December.
    68. Ferraz, Rosemeire O. & Hotta, Luiz K., 2007. "Quasi-Maximum Likelihood Estimation of Long-Memory Stochastic Volatility Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 27(2), November.
    69. Less, Vivien & Sibbertsen, Philipp, 2022. "Estimation and Testing in a Perturbed Multivariate Long Memory Framework," Hannover Economic Papers (HEP) dp-704, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    70. 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.
    71. Yanlin Shi, 2023. "Long memory and regime switching in the stochastic volatility modelling," Annals of Operations Research, Springer, vol. 320(2), pages 999-1020, January.
    72. Josu Arteche & Jesus Orbe, 2009. "Bootstrap‐based bandwidth choice for log‐periodogram regression," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 591-617, November.
    73. Milan Bašta, 2012. "Wavelets and Estimation of Long Memory in Log Volatility and Time Series Perturbed by Noise," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2012(2), pages 3-20.

  28. Clifford M. Hurvich, 2001. "Model Selection for Broadband Semiparametric Estimation of Long Memory in Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(6), pages 679-709, November.

    Cited by:

    1. Violetta Dalla & Liudas Giraitis & Javier Hidalgo, 2006. "Consistent estimation of the memory parameterfor nonlinear time series," STICERD - Econometrics Paper Series 497, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Hidalgo, J., 2008. "Specification testing for regression models with dependent data," Journal of Econometrics, Elsevier, vol. 143(1), pages 143-165, March.
    3. Hidalgo, Javier, 2007. "Specification testing for regression models with dependent data," LSE Research Online Documents on Economics 6799, London School of Economics and Political Science, LSE Library.
    4. Dalla, Violetta & Giraitis, Liudas & Hidalgo, Javier, 2006. "Consistent estimation of the memory parameter for nonlinear time series," LSE Research Online Documents on Economics 6813, London School of Economics and Political Science, LSE Library.
    5. Liudas Giraitis & Peter M Robinson, 2002. "Edgeworth Expansions for Semiparametric Whittle Estimation of Long Memory," STICERD - Econometrics Paper Series 438, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    6. Giraitis, L. & Robinson, P.M., 2003. "Edgeworth expansions for semiparametric Whittle estimation of long memory," LSE Research Online Documents on Economics 291, London School of Economics and Political Science, LSE Library.
    7. Javier Hidalgo, 2007. "Specification Testing Forregression Models Withdependent Data," STICERD - Econometrics Paper Series 518, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    8. Jan Beran & Sucharita Ghosh, 2020. "Estimating the Mean Direction of Strongly Dependent Circular Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 210-228, March.
    9. Giraitis, Liudas & Robinson, Peter, 2002. "Edgeworth expansions for semiparametric Whittle estimation of long memory," LSE Research Online Documents on Economics 2130, London School of Economics and Political Science, LSE Library.
    10. Chen, Willa W. & Hurvich, Clifford M., 2003. "Estimating fractional cointegration in the presence of polynomial trends," Journal of Econometrics, Elsevier, vol. 117(1), pages 95-121, November.
    11. Saeed Heravi & Kerry Patterson, 2005. "Optimal And Adaptive Semi‐Parametric Narrowband And Broadband And Maximum Likelihood Estimation Of The Long‐Memory Parameter For Real Exchange Rates," Manchester School, University of Manchester, vol. 73(2), pages 165-213, March.
    12. Angela Ferretti & L. Ippoliti & P. Valentini & R. J. Bhansali, 2023. "Long memory conditional random fields on regular lattices," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    13. Masaki Narukawa & Yasumasa Matsuda, 2008. "Broadband semiparametric estimation of the long-memory parameter by the likelihood-based FEXP approach," TERG Discussion Papers 239, Graduate School of Economics and Management, Tohoku University.

  29. Clifford M. Hurvich & Willa W. Chen, 2000. "An Efficient Taper for Potentially Overdifferenced Long‐memory Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(2), pages 155-180, March.

    Cited by:

    1. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
    2. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.
    3. Christian Leschinski & Michelle Voges & Philipp Sibbertsen, 2021. "A comparison of semiparametric tests for fractional cointegration," Statistical Papers, Springer, vol. 62(4), pages 1997-2030, August.
    4. Gannaz, Irène, 2023. "Asymptotic normality of wavelet covariances and multivariate wavelet Whittle estimators," Stochastic Processes and their Applications, Elsevier, vol. 155(C), pages 485-534.
    5. Willa Chen & Clifford Hurvich, 2004. "Semiparametric Estimation of Fractional Cointegrating Subspaces," Econometrics 0412007, University Library of Munich, Germany.
    6. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2005. "Estimating Long Memory in Volatility," Econometrica, Econometric Society, vol. 73(4), pages 1283-1328, July.
    7. Alia Afzal & Philipp Sibbertsen, 2021. "Modeling fractional cointegration between high and low stock prices in Asian countries," Empirical Economics, Springer, vol. 60(2), pages 661-682, February.
    8. Guillermo Ferreira & Jorge Mateu & Jose A. Vilar & Joel Muñoz, 2021. "Bootstrapping regression models with locally stationary disturbances," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 341-363, June.
    9. Carlos Velasco, 2003. "Gaussian Semi‐parametric Estimation of Fractional Cointegration," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 345-378, May.
    10. Saeed Heravi & Kerry Patterson, 2005. "Optimal And Adaptive Semi‐Parametric Narrowband And Broadband And Maximum Likelihood Estimation Of The Long‐Memory Parameter For Real Exchange Rates," Manchester School, University of Manchester, vol. 73(2), pages 165-213, March.
    11. Arteche González, Jesús María, 2020. "Frequency Domain Local Bootstrap in long memory time series," BILTOKI info:eu-repo/grantAgreeme, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).

  30. Clifford M. Hurvich & Rohit S. Deo, 1999. "Plug‐in Selection of the Number of Frequencies in Regression Estimates of the Memory Parameter of a Long‐memory Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(3), pages 331-341, May.

    Cited by:

    1. D. (Derek) Bond & Michael J. Harrison & Edward J. (Edward Joseph) O'Brien, 2009. "Exploring long memory and nonlinearity in Irish real exchange Rates using tests based on semiparametric estimation," Working Papers 200901, School of Economics, University College Dublin.
    2. Berna Kirkulak Uludag & Zorikto Lkhamazhapov, 2014. "Long memory and structural breaks in the returns and volatility of gold: evidence from Turkey," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3777-3787, November.
    3. Hassler, U. & Marmol, F. & Velasco, C., 2006. "Residual log-periodogram inference for long-run relationships," Journal of Econometrics, Elsevier, vol. 130(1), pages 165-207, January.
    4. Arteche, Josu & Orbe, Jesus, 2016. "A bootstrap approximation for the distribution of the Local Whittle estimator," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 645-660.
    5. Bond, Derek & Gallagher, Emer & Ramsey, Elaine, 2012. "A preliminary investigation of northern Ireland's housing market dynamics," MPRA Paper 39806, University Library of Munich, Germany.
    6. Arteche, Josu & Orbe, Jesus, 2017. "A strategy for optimal bandwidth selection in Local Whittle estimation," Econometrics and Statistics, Elsevier, vol. 4(C), pages 3-17.
    7. Arteche, Josu & Orbe, Jesus, 2009. "Using the bootstrap for finite sample confidence intervals of the log periodogram regression," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1940-1953, April.
    8. James Davidson & Philipp Sibbertsen, 2008. "Tests of Bias in Log-Periodogram Regression," Discussion Papers 0805, University of Exeter, Department of Economics.
    9. Franco, Glaura C. & Reisen, Valderio A., 2007. "Bootstrap approaches and confidence intervals for stationary and non-stationary long-range dependence processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 546-562.
    10. Reisen Valderio A & Cribari-Neto Francisco & Jensen Mark J, 2003. "Long Memory Inflationary Dynamics: The Case of Brazil," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(3), pages 1-18, October.
    11. Dräger, Lena & Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2020. "The Long Memory of Equity Volatility and the Macroeconomy: International Evidence," Hannover Economic Papers (HEP) dp-667, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    12. Peter M Robinson, 2004. "ROBUST COVARIANCE MATRIX ESTIMATION: "HAC" Estimates with Long Memory/Antipersistence Correction," STICERD - Econometrics Paper Series 471, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2017. "The Long Memory of Equity Volatility: International Evidence," Hannover Economic Papers (HEP) dp-614, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    14. Ye, Xunyu & Gao, Ping & Li, Handong, 2015. "Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram," Economic Modelling, Elsevier, vol. 46(C), pages 167-179.
    15. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.
    16. Saeed Heravi & Kerry Patterson, 2013. "Log-Periodogram Estimation of the Long-Memory Parameter: An Evaluation of Competing Estimators," Economics Discussion Papers em-dp2013-02, Department of Economics, University of Reading.
    17. Lopes, Sílvia Regina Costa & Olbermann, Bárbara Patrícia & Reisen, Valderio Anselmo, 2002. "Non-stationary Gaussian ARFIMA processes: Estimation and application," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 22(1), May.
    18. Jiang, George J. & Tian, Yisong S., 2010. "Forecasting Volatility Using Long Memory and Comovements: An Application to Option Valuation under SFAS 123R," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 503-533, April.
    19. Uwe Hassler & Marc-Oliver Pohle, 2019. "Forecasting under Long Memory and Nonstationarity," Papers 1910.08202, arXiv.org.
    20. Rohit Deo & Mengchen Hsieh & Clifford Hurvich, 2005. "Tracing the Source of Long Memory in Volatility," Econometrics 0501005, University Library of Munich, Germany.
    21. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2017. "The Memory of Stock Return Volatility: Asset Pricing Implications," Hannover Economic Papers (HEP) dp-613, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    22. Robinson, Peter M., 2004. "Robust covariance matrix estimation : HAC estimates with long memory/antipersistence correction," LSE Research Online Documents on Economics 2157, London School of Economics and Political Science, LSE Library.
    23. Yixun Xing & Wayne A. Woodward, 2021. "R-Squared-Bootstrapping for Gegenbauer-Type Long Memory," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 773-790, February.
    24. Assaf, Ata, 2015. "Long memory and level shifts in REITs returns and volatility," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 172-182.
    25. Omane-Adjepong, Maurice & Alagidede, Paul & Akosah, Nana Kwame, 2019. "Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 105-120.
    26. Dooruj Rambaccussing & Murat Mazibas, 2020. "True versus Spurious Long Memory in Cryptocurrencies," JRFM, MDPI, vol. 13(9), pages 1-11, August.
    27. Clements, A.E. & Hurn, A.S. & Volkov, V.V., 2016. "Common trends in global volatility," Journal of International Money and Finance, Elsevier, vol. 67(C), pages 194-214.
    28. Arteche González, Jesús María & Orbe Lizundia, Jesús María, 2008. "Selection of the number of frequencies using bootstrap techniques in log-periodogram regression," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    29. Masaki Narukawa & Yasumasa Matsuda, 2008. "Broadband semiparametric estimation of the long-memory parameter by the likelihood-based FEXP approach," TERG Discussion Papers 239, Graduate School of Economics and Management, Tohoku University.
    30. 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.
    31. Philipp Sibbertsen, 2004. "Long memory in volatilities of German stock returns," Empirical Economics, Springer, vol. 29(3), pages 477-488, September.
    32. Sibbertsen, Philipp & Venetis, Ioannis, 2003. "Distinguishing between long-range dependence and deterministic trends," Technical Reports 2003,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

  31. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.

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    1. Alethea Rea & William Rea & Marco Reale & Carl Scarrott, 2012. "A comparison of Spillover Effects before, during and after the 2008 Financial Crisis," Working Papers in Economics 12/03, University of Canterbury, Department of Economics and Finance.
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    3. Ivan Chang, Yuan-Chin & Huang, Yufen & Huang, Yu-Pai, 2010. "Early stopping in L2Boosting," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2203-2213, October.
    4. Flavio Calvino & Chiara Criscuolo & Carlo Menon & Angelo Secchi, 2018. "Growth volatility and size: A firm-level study," PSE-Ecole d'économie de Paris (Postprint) halshs-01802871, HAL.
    5. Germán Ibacache-Pulgar & Gilberto Paula & Francisco Cysneiros, 2013. "Semiparametric additive models under symmetric distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 103-121, March.
    6. Jeff Racine, 2006. "gnuplot 4.0: a portable interactive plotting utility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 133-141.
    7. Deniz Baglan & Hakan Yilmazkuday, 2016. "Financial Health and the Intensive Margin of Trade," Working Papers 1607, Florida International University, Department of Economics.
    8. Jang, Dongik & Oh, Hee-Seok, 2011. "Enhancement of spatially adaptive smoothing splines via parameterization of smoothing parameters," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1029-1040, February.
    9. James, Ryan D. & Campbell Jr., Harrison S., 2016. "Exploring the Role of Unearned and Non-Wage Income on Regional Income Convergence," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 46(2), December.
    10. Daye, Z. John & Jeng, X. Jessie, 2009. "Shrinkage and model selection with correlated variables via weighted fusion," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1284-1298, February.
    11. Hans R. A. Koster & Piet Rietveld & Jos Van Ommeren, 2013. "Historic Amenities, Income and Sorting of Households," SERC Discussion Papers 0124, Centre for Economic Performance, LSE.
    12. Bethany Everett & David Rehkopf & Richard Rogers, 2013. "The Nonlinear Relationship Between Education and Mortality: An Examination of Cohort, Race/Ethnic, and Gender Differences," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 32(6), pages 893-917, December.
    13. Franklin, Jessica M. & Schneeweiss, Sebastian & Polinski, Jennifer M. & Rassen, Jeremy A., 2014. "Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 219-226.
    14. Li, Deng-Kui & Mei, Chang-Lin & Wang, Ning, 2019. "Tests for spatial dependence and heterogeneity in spatially autoregressive varying coefficient models with application to Boston house price analysis," Regional Science and Urban Economics, Elsevier, vol. 79(C).
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    16. Tsimpanos, Apostolos & Tsimbos, Cleon & Kalogirou, Stamatis, 2018. "Assessing spatial variation and heterogeneity of fertility in Greece at local authority level," MPRA Paper 100406, University Library of Munich, Germany.
    17. Liu, Sisheng & Kong, Xiaoli, 2022. "A generalized correlated Cp criterion for derivative estimation with dependent errors," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
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    23. Henderson, Daniel J., 2008. "A Test for Multimodality of Regression Derivatives with an Application to Nonparametric Growth Regressions," MPRA Paper 8768, University Library of Munich, Germany.
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    25. Michael S. Delgado & Daniel J. Henderson & Christopher F. Parmeter, 2014. "Does Education Matter for Economic Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 334-359, June.
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    27. Hans Koster & Jos N. van Ommeren & Piet Rietveld, 2010. "Estimating Firms' Demand for Agglomeration," Tinbergen Institute Discussion Papers 10-087/3, Tinbergen Institute.
    28. Fabian Gouret, 2019. "Empirical foundation of valence using Aldrich-McKelvey scaling," THEMA Working Papers 2019-10, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    29. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    30. 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.
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    49. Koo, Chao, 2018. "Essays on functional coefficient models," Other publications TiSEM ba87b8a5-3c55-40ec-967d-9, Tilburg University, School of Economics and Management.
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    3. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
    4. Reisen, Valdério A. & Zamprogno, Bartolomeu & Palma, Wilfredo & Arteche, Josu, 2014. "A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 1-17.
    5. Kanchana Nadarajah & Gael M Martin & Donald S Poskitt, 2019. "Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach," Monash Econometrics and Business Statistics Working Papers 7/19, Monash University, Department of Econometrics and Business Statistics.
    6. McMillan, David G., 2009. "Are share prices still too high?," Research in International Business and Finance, Elsevier, vol. 23(3), pages 223-232, September.
    7. Robinson Kruse & Philipp Sibbertsen, 2010. "Long memory and changing persistence," CREATES Research Papers 2010-42, Department of Economics and Business Economics, Aarhus University.
    8. Peter C. B. Phillips & Zhijie Xiao, 1998. "A Primer on Unit Root Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 423-470, December.
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    39. Cevik, Emrah Ismail, 2012. "İstanbul Menkul Kıymetler Borsası’nda etkin piyasa hipotezinin uzun hafıza modelleri ile analizi: sektörel bazda bir inceleme [The testing of efficient market hypothesis in the Istanbul Stock Excha," MPRA Paper 71484, University Library of Munich, Germany, revised 2012.
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    127. Masaki Narukawa & Yasumasa Matsuda, 2008. "Broadband semiparametric estimation of the long-memory parameter by the likelihood-based FEXP approach," TERG Discussion Papers 239, Graduate School of Economics and Management, Tohoku University.
    128. Stilian Stoev & Murad S. Taqqu, 2005. "Asymptotic self‐similarity and wavelet estimation for long‐range dependent fractional autoregressive integrated moving average time series with stable innovations," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 211-249, March.
    129. Uematsu, Yoshimasa, 2016. "Asymptotic efficiency of the OLS estimator with singular limiting sample moment matrices," Statistics & Probability Letters, Elsevier, vol. 114(C), pages 104-110.
    130. 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.
    131. Arteche, J. & Orbe, J., 2005. "Bootstrapping the log-periodogram regression," Economics Letters, Elsevier, vol. 86(1), pages 79-85, January.
    132. Aeneas Rooch & Ieva Zelo & Roland Fried, 2019. "Estimation methods for the LRD parameter under a change in the mean," Statistical Papers, Springer, vol. 60(1), pages 313-347, February.
    133. Philipp Sibbertsen, 2004. "Long memory in volatilities of German stock returns," Empirical Economics, Springer, vol. 29(3), pages 477-488, September.
    134. Yaeji Lim & Hee-Seok Oh, 2022. "Quantile spectral analysis of long-memory processes," Empirical Economics, Springer, vol. 62(3), pages 1245-1266, March.
    135. Heinen, Florian & Willert, Juliane, 2011. "Monitoring a change in persistence of a long range dependent time series," Hannover Economic Papers (HEP) dp-479, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    136. Sibbertsen, Philipp & Venetis, Ioannis, 2003. "Distinguishing between long-range dependence and deterministic trends," Technical Reports 2003,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

  33. Rohit S. Deo & Clifford M. Hurvich, 1998. "Linear Trend with Fractionally Integrated Errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(4), pages 379-397, July.

    Cited by:

    1. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2011. "An I(d) model with trend and cycles," Journal of Econometrics, Elsevier, vol. 163(2), pages 186-199, August.
    2. Beran, Jan & Feng, Yuanhua, 2002. "SEMIFAR models--a semiparametric approach to modelling trends, long-range dependence and nonstationarity," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 393-419, August.
    3. Karim M. Abadir & Walter Distaso & Liudas Giraitis, 2011. "An I() model with trend and cycles," Post-Print hal-00834425, HAL.
    4. Jiang, George J. & Tian, Yisong S., 2010. "Forecasting Volatility Using Long Memory and Comovements: An Application to Option Valuation under SFAS 123R," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 503-533, April.
    5. Beran, Jan, 1999. "SEMIFAR Models - A Semiparametric Framework for Modelling Trends, Long Range Dependence and Nonstationarity," CoFE Discussion Papers 99/16, University of Konstanz, Center of Finance and Econometrics (CoFE).
    6. Hwai‐Chung Ho & Nan‐Jung Hsu, 2005. "Polynomial Trend Regression With Long‐memory Errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(3), pages 323-354, May.
    7. Papailias, Fotis & Fruet Dias, Gustavo, 2015. "Forecasting long memory series subject to structural change: A two-stage approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1056-1066.
    8. Chen, Willa W. & Hurvich, Clifford M., 2003. "Estimating fractional cointegration in the presence of polynomial trends," Journal of Econometrics, Elsevier, vol. 117(1), pages 95-121, November.

  34. Hurvich, Clifford M. & Tsai, Chih-Ling, 1996. "The impact of unsuspected serial correlations on model selection in linear regression," Statistics & Probability Letters, Elsevier, vol. 27(2), pages 115-126, April.

    Cited by:

    1. H. Burcu Gurcihan & Gonul Sengul & Arzu Yavuz, 2013. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Working Papers 1341, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

  35. Clifford M. Hurvich & Bonnie K. Ray, 1995. "Estimation Of The Memory Parameter For Nonstationary Or Noninvertible Fractionally Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(1), pages 17-41, January.

    Cited by:

    1. Per Frederiksen & Morten Orregaard Nielsen, 2008. "Bias-Reduced Estimation of Long-Memory Stochastic Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 496-512, Fall.
    2. Ingolf Dittmann, 2000. "Residual‐Based Tests For Fractional Cointegration: A Monte Carlo Study," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(6), pages 615-647, November.
    3. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Is market fear persistent? A long-memory analysis," Finance Research Letters, Elsevier, vol. 27(C), pages 140-147.
    4. Peter M Robinson & Carlos Velasco, 2000. "Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series - (Now published in Journal of the American Statistical Association, 95, (2000), pp.1229-1243.)," STICERD - Econometrics Paper Series 391, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. Hassler, U. & Marmol, F. & Velasco, C., 2006. "Residual log-periodogram inference for long-run relationships," Journal of Econometrics, Elsevier, vol. 130(1), pages 165-207, January.
    6. Jeng, Jau-Lian, 1999. "Interest parity, fractional differencing, and the strength of attraction: a reexamination of the cost-of-carry futures pricing model," Global Finance Journal, Elsevier, vol. 10(1), pages 25-34.
    7. Velasco, Carlos, 1998. "Non-stationary log-periodogram regression," DES - Working Papers. Statistics and Econometrics. WS 4554, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Terence Tai-Leung, Chong, 1998. "Estimating the Differencing Parameter Via the Partial Autocorrelation Function," Departmental Working Papers _088, Chinese University of Hong Kong, Department of Economics.
    9. Guglielmo Maria Caporale & Luis A. Gil-Alana & Alex Plastun, 2017. "Long Memory and Data Frequency in Financial Markets," Discussion Papers of DIW Berlin 1647, DIW Berlin, German Institute for Economic Research.
    10. Gael Martin, 2001. "Bayesian Analysis Of A Fractional Cointegration Model," Econometric Reviews, Taylor & Francis Journals, vol. 20(2), pages 217-234.
    11. Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2016. "Time series analysis of volatility in the petroleum pricing markets: the persistence, asymmetry and jumps in the returns series," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 40(3), pages 235-262, September.
    12. Chang Sik Kim & Peter C.B. Phillips, 2006. "Log Periodogram Regression: The Nonstationary Case," Cowles Foundation Discussion Papers 1587, Cowles Foundation for Research in Economics, Yale University.
    13. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    14. Peter C.B. Phillips, 1999. "Unit Root Log Periodogram Regression," Cowles Foundation Discussion Papers 1244, Cowles Foundation for Research in Economics, Yale University.
    15. Kühl, Michael, 2008. "Strong comovements of exchange rates: Theoretical and empirical cases when currencies become the same asset," University of Göttingen Working Papers in Economics 76, University of Goettingen, Department of Economics.
    16. Franco, Glaura C. & Reisen, Valderio A., 2007. "Bootstrap approaches and confidence intervals for stationary and non-stationary long-range dependence processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 546-562.
    17. Frank S. Nielsen, 2011. "Local Whittle estimation of multi‐variate fractionally integrated processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 317-335, May.
    18. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009. "Long Memory in US Real Output per Capita," CESifo Working Paper Series 2671, CESifo.
    19. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.
    20. Giraitis, Liudas & Robinson, Peter M. & Samarov, Alexander, 2000. "Adaptive Semiparametric Estimation of the Memory Parameter," Journal of Multivariate Analysis, Elsevier, vol. 72(2), pages 183-207, February.
    21. Arielle Beyaert, 2004. "Fractional Output Convergence, with an Application to Nine Developed Countries," Econometric Society 2004 Australasian Meetings 280, Econometric Society.
    22. Ye, Xunyu & Gao, Ping & Li, Handong, 2015. "Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram," Economic Modelling, Elsevier, vol. 46(C), pages 167-179.
    23. Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, May.
    24. Gilles Dufrénot & Valérie Mignon & Théo Naccache, 2012. "The slow convergence of per capita income between the developing countries: ‘growth resistance’ and sometimes ‘growth tragedy’," Post-Print hal-01385800, HAL.
    25. Zargar, Faisal Nazir & Kumar, Dilip, 2019. "Long range dependence in the Bitcoin market: A study based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 625-640.
    26. Lopes, Sílvia Regina Costa & Olbermann, Bárbara Patrícia & Reisen, Valderio Anselmo, 2002. "Non-stationary Gaussian ARFIMA processes: Estimation and application," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 22(1), May.
    27. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2005. "Estimating Long Memory in Volatility," Econometrica, Econometric Society, vol. 73(4), pages 1283-1328, July.
    28. Liudas Giraitis & Peter M Robinson, 2002. "Edgeworth Expansions for Semiparametric Whittle Estimation of Long Memory," STICERD - Econometrics Paper Series 438, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    29. Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
    30. Eduardo Rossi & Paolo Santucci de Magistris, 2012. "Estimation of long memory in integrated variance," DEM Working Papers Series 017, University of Pavia, Department of Economics and Management.
    31. Valle e Azevedo, João, 2007. "Interpretation of the Effects of Filtering Integrated Time Series," MPRA Paper 6574, University Library of Munich, Germany.
    32. Giraitis, L. & Robinson, P.M., 2003. "Edgeworth expansions for semiparametric Whittle estimation of long memory," LSE Research Online Documents on Economics 291, London School of Economics and Political Science, LSE Library.
    33. Robinson, Peter M. & Velasco, Carlos, 2000. "Whittle pseudo-maximum likelihood estimation for nonstationary time series," LSE Research Online Documents on Economics 2273, London School of Economics and Political Science, LSE Library.
    34. Gilles de Truchis, 2012. "Approximate Whittle Analysis of Fractional Cointegration and the Stock Market Synchronization Issue," Working Papers halshs-00793220, HAL.
    35. Guillermo Ferreira & Jorge Mateu & Jose A. Vilar & Joel Muñoz, 2021. "Bootstrapping regression models with locally stationary disturbances," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 341-363, June.
    36. Peter M Robinson, 2004. "The Distance between Rival Nonstationary Fractional Processes," STICERD - Econometrics Paper Series 468, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    37. Sibbertsen, Philipp, 2001. "Log-periodogram estimation of the memory parameter of a long-memory process under trend," Technical Reports 2001,39, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    38. Valle e Azevedo, João, 2007. "Exact Limit of the Expected Periodogram in the Unit-Root Case," MPRA Paper 6553, University Library of Munich, Germany.
    39. C. Bisognin & S. R. C. Lopes, 2007. "Estimating and forecasting the long-memory parameter in the presence of periodicity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(6), pages 405-427.
    40. Margherita Gerolimetto & Stefano Magrini, 2020. "Testing for boundary conditions in case of fractionally integrated processes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 357-371, June.
    41. Frank S. Nielsen, 2008. "Local polynomial Whittle estimation covering non-stationary fractional processes," CREATES Research Papers 2008-28, Department of Economics and Business Economics, Aarhus University.
    42. Hurvich, Clifford M. & Moulines, Eric & Soulier, Philippe, 2002. "The FEXP estimator for potentially non-stationary linear time series," Stochastic Processes and their Applications, Elsevier, vol. 97(2), pages 307-340, February.
    43. Katsumi Shimotsu, 2006. "Exact Local Whittle Estimation of Fractional Integration with Unknown Mean and Time Trend," Working Paper 1061, Economics Department, Queen's University.
    44. Shimotsu, Katsumi & Phillips, Peter C.B., 2006. "Local Whittle estimation of fractional integration and some of its variants," Journal of Econometrics, Elsevier, vol. 130(2), pages 209-233, February.
    45. Yixun Xing & Wayne A. Woodward, 2021. "R-Squared-Bootstrapping for Gegenbauer-Type Long Memory," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 773-790, February.
    46. Franco, G.C. & Reisen, V.A. & Alves, F.A., 2013. "Bootstrap tests for fractional integration and cointegration: A comparison study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 87(C), pages 19-29.
    47. Frederiksen, Per & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2012. "Local polynomial Whittle estimation of perturbed fractional processes," Journal of Econometrics, Elsevier, vol. 167(2), pages 426-447.
    48. Kufenko, Vadim, 2016. "Spurious periodicities in cliometric series: Simultaneous testing," Violette Reihe: Schriftenreihe des Promotionsschwerpunkts "Globalisierung und Beschäftigung" 48/2016, University of Hohenheim, Carl von Ossietzky University Oldenburg, Evangelisches Studienwerk.
    49. Giraitis, Liudas & Robinson, Peter, 2002. "Edgeworth expansions for semiparametric Whittle estimation of long memory," LSE Research Online Documents on Economics 2130, London School of Economics and Political Science, LSE Library.
    50. Régis Bourbonnais & Magda Mara Maftei, 2012. "ARFIMA Process : Tests and Applications at a White Noise Process, A Random Walk Process and the Stock Exchange Index CAC 40," Post-Print hal-01491880, HAL.
    51. Robinson, P.M., 2005. "The distance between rival nonstationary fractional processes," Journal of Econometrics, Elsevier, vol. 128(2), pages 283-300, October.
    52. Maria Caporale, Guglielmo & Gil-Alana, Luis & Plastun, Alex & Makarenko, Inna, 2013. "Long memory in the ukrainian stock market and financial crises," MPRA Paper 59061, University Library of Munich, Germany.
    53. Velasco, Carlos, 1998. "Non-Gaussian log-periodogram regression," DES - Working Papers. Statistics and Econometrics. WS 4553, Universidad Carlos III de Madrid. Departamento de Estadística.
    54. João Valle e Azevedo, 2007. "A Multivariate Band-Pass Filter," Working Papers w200717, Banco de Portugal, Economics and Research Department.
    55. Chen, Willa W. & Hurvich, Clifford M., 2003. "Estimating fractional cointegration in the presence of polynomial trends," Journal of Econometrics, Elsevier, vol. 117(1), pages 95-121, November.
    56. Robinson, Peter M., 2004. "The distance between rival nonstationary fractional processes," LSE Research Online Documents on Economics 2282, London School of Economics and Political Science, LSE Library.

  36. Clifford M. Hurvich & Kaizo I. Beltrao, 1994. "Automatic Semiparametric Estimation Of The Memory Parameter Of A Long‐Memory Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(3), pages 285-302, May.

    Cited by:

    1. Christian Leschinski & Michelle Voges & Philipp Sibbertsen, 2021. "Integration and Disintegration of EMU Government Bond Markets," Econometrics, MDPI, vol. 9(1), pages 1-17, March.
    2. Silverberg, Gerald & Verspagen, Bart, 2000. "A Note on Michelacci and Zaffaroni, Long Memory, and Time Series of Economic Growth," Research Memorandum 031, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    3. Feng, Yuanhua & Beran, Jan, 2008. "Filtered Log-periodogram Regression of long memory processes," CoFE Discussion Papers 08/10, University of Konstanz, Center of Finance and Econometrics (CoFE).
    4. Saeed Heravi & Kerry Patterson, 2005. "Optimal And Adaptive Semi‐Parametric Narrowband And Broadband And Maximum Likelihood Estimation Of The Long‐Memory Parameter For Real Exchange Rates," Manchester School, University of Manchester, vol. 73(2), pages 165-213, March.
    5. Erhard Reschenhofer & Manveer Kaur Mangat & Christian Zwatz & Sándor Guzmics, 2020. "Evaluation of current research on stock return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 334-351, March.

  37. Terrin, Norma & Hurvich, Clifford M., 1994. "An asymptotic Wiener-Itô representation for the low frequency ordinates of the periodogram of a long memory time series," Stochastic Processes and their Applications, Elsevier, vol. 54(2), pages 297-307, December.

    Cited by:

    1. Willa Chen & Clifford Hurvich, 2004. "Semiparametric Estimation of Fractional Cointegrating Subspaces," Econometrics 0412007, University Library of Munich, Germany.
    2. Gabriel Lang & Philippe Soulier, 2000. "Convergence de mesures spectrales aléatoires et applications à des principes d'invariance," Statistical Inference for Stochastic Processes, Springer, vol. 3(1), pages 41-51, January.
    3. Hsieh, Meng-Chen & Hurvich, Clifford M. & Soulier, Philippe, 2007. "Asymptotics for duration-driven long range dependent processes," Journal of Econometrics, Elsevier, vol. 141(2), pages 913-949, December.

  38. Clifford M. Hurvich & Kaizo I. Beltrao, 1993. "Asymptotics For The Low‐Frequency Ordinates Of The Periodogram Of A Long‐Memory Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(5), pages 455-472, September.

    Cited by:

    1. Francis In & Sangbae Kim, 2012. "An Introduction to Wavelet Theory in Finance:A Wavelet Multiscale Approach," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8431, January.
    2. Grace Yap & Wen Cheong Chin, 2016. "Spectral bandwidth selection for long memory," Modern Applied Science, Canadian Center of Science and Education, vol. 10(8), pages 1-63, August.
    3. Mark J. Jensen, 1997. "Using Wavelets to Obtain a Consistent Ordinary Least Squares Estimator of the Long Memory Parameter," Econometrics 9710002, University Library of Munich, Germany.
    4. Erhard Reschenhofer & Thomas Stark & Manveer K. Mangat, 2020. "Robust Estimation of the Memory Parameter," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(4), pages 1-5.
    5. Aeneas Rooch & Ieva Zelo & Roland Fried, 2019. "Estimation methods for the LRD parameter under a change in the mean," Statistical Papers, Springer, vol. 60(1), pages 313-347, February.
    6. Yaeji Lim & Hee-Seok Oh, 2022. "Quantile spectral analysis of long-memory processes," Empirical Economics, Springer, vol. 62(3), pages 1245-1266, March.

  39. Clifford M. Hurvich & Chih‐Ling Tsai, 1993. "A Corrected Akaike Information Criterion For Vector Autoregressive Model Selection," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(3), pages 271-279, May.

    Cited by:

    1. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Cavanaugh, Joseph E., 1997. "Unifying the derivations for the Akaike and corrected Akaike information criteria," Statistics & Probability Letters, Elsevier, vol. 33(2), pages 201-208, April.
    3. Racicot, François-Éric & Théoret, Raymond, 2018. "Multi-moment risk, hedging strategies, & the business cycle," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 637-675.
    4. Bengtsson, Thomas & Cavanaugh, Joseph E., 2006. "An improved Akaike information criterion for state-space model selection," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2635-2654, June.
    5. Han Lin Shang, 2023. "Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 421-441, September.
    6. M. S. Eliwa & M. El-Morshedy & Mohamed Ibrahim, 2019. "Inverse Gompertz Distribution: Properties and Different Estimation Methods with Application to Complete and Censored Data," Annals of Data Science, Springer, vol. 6(2), pages 321-339, June.
    7. Pennoni, Fulvia & Bartolucci, Francesco & Forte, Gianfranco & Ametrano, Ferdinando, 2020. "Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model," MPRA Paper 106150, University Library of Munich, Germany.
    8. Cavanaugh, Joseph E., 1999. "A large-sample model selection criterion based on Kullback's symmetric divergence," Statistics & Probability Letters, Elsevier, vol. 42(4), pages 333-343, May.
    9. Kirsten Lommatzsch & Silke Tober, 2004. "The Inflation Target of the ECB: Does the Balassa-Samuelson Effect Matter?," EUI-RSCAS Working Papers 19, European University Institute (EUI), Robert Schuman Centre of Advanced Studies (RSCAS).
    10. François-Éric Racicot & Raymond Théoret, 2022. "Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios: a nonlinear VAR approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    11. Yifei Cai & Jamel Saadaoui & Yanrui Wu, 2022. "The Political Relation and Trade - The Case of US, China and Australia," Working Papers of BETA 2022-22, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    12. Òscar Jordà & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.
    13. Shang, Junfeng & Cavanaugh, Joseph E., 2008. "Bootstrap variants of the Akaike information criterion for mixed model selection," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2004-2021, January.
    14. An Hoai Duong & Ernoiz Antriyandarti, 2023. "The Willingness to get Vaccinated Against SARS-CoV-2 Virus among Southeast Asian Countries: Does the Vaccine Brand Matter?," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 18(2), pages 765-793, April.
    15. Carlos A. Medel, 2015. "Probabilidad Clásica de Sobreajuste con Criterios de Información: Estimaciones con Series Macroeconómicas Chilenas," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 30(1), pages 57-72, Abril.
    16. Xiaowen Jin & Liang Wang & Zhengzheng Zhang & Jingzhuang Yan, 2022. "Factors Affecting the Income of Agritourism Operations: Evidence from an Eastern Chinese County," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    17. Daniel Grabowski & Anna Staszewska-Bystrova & Peter Winker, 2020. "Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 5-32, March.
    18. Kokoszka, Piotr & Miao, Hong & Petersen, Alexander & Shang, Han Lin, 2019. "Forecasting of density functions with an application to cross-sectional and intraday returns," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1304-1317.
    19. Calmès, Christian & Théoret, Raymond, 2020. "Bank fee-based shocks and the U.S. business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    20. Yifei Cai & Jamel Saadaoui & Yanrui Wu, 2023. "Political Relations and Trade: New Evidence from Australia, China and the United States," Working Papers of BETA 2023-24, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    21. Alok Tiwari & Mohammed Aljoufie, 2021. "Modeling Spatial Distribution and Determinant of PM 2.5 at Micro-Level Using Geographically Weighted Regression (GWR) to Inform Sustainable Mobility Policies in Campus Based on Evidence from King Abdu," Sustainability, MDPI, vol. 13(21), pages 1-14, October.
    22. Boubacar Mainassara, Yacouba, 2010. "Selection of weak VARMA models by modified Akaike's information criteria," MPRA Paper 24981, University Library of Munich, Germany.
    23. Nan Li & Simon S. Kwok, 2021. "Jointly determining the state dimension and lag order for Markov‐switching vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 471-491, July.
    24. Hafidi, Bezza, 2006. "A small-sample criterion based on Kullback's symmetric divergence for vector autoregressive modeling," Statistics & Probability Letters, Elsevier, vol. 76(15), pages 1647-1654, September.
    25. Jonathan E Bone & Katherine McAuliffe & Nichola J Raihani, 2016. "Exploring the Motivations for Punishment: Framing and Country-Level Effects," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
    26. Han Lin Shang, 2024. "Bootstrapping Long-Run Covariance of Stationary Functional Time Series," Forecasting, MDPI, vol. 6(1), pages 1-14, February.
    27. Jonathan E Bone & Brian Wallace & Redouan Bshary & Nichola J Raihani, 2016. "Power Asymmetries and Punishment in a Prisoner’s Dilemma with Variable Cooperative Investment," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-16, May.
    28. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, CEMLA, vol. 0(4), pages 517-589, octubre-d.
    29. Hafidi, B. & Mkhadri, A., 2006. "A corrected Akaike criterion based on Kullback's symmetric divergence: applications in time series, multiple and multivariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1524-1550, March.
    30. Tamara Burdisso & Eduardo Ariel Corso, 2011. "Incertidumbre y dolarización de cartera: el caso argentino en el último medio siglo," Monetaria, CEMLA, vol. 0(4), pages 461-515, octubre-d.
    31. Klemens Hauzenberger & Robert Stehrer, 2010. "An Empirical Characterization of Redistribution Shocks and Output Dynamics," wiiw Working Papers 68, The Vienna Institute for International Economic Studies, wiiw.
    32. Oscar Jorda, 2004. "Model-Free Impulse Responses," Working Papers 87, University of California, Davis, Department of Economics.
    33. Marhuenda, Yolanda & Morales, Domingo & del Carmen Pardo, María, 2014. "Information criteria for Fay–Herriot model selection," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 268-280.
    34. Medel, Carlos A., 2012. "¿Akaike o Schwarz? ¿Cuál elegir para predecir el PIB chileno? [Akaike or Schwarz? Which One is a Better Predictor of Chilean GDP?]," MPRA Paper 35950, University Library of Munich, Germany.
    35. Stefan Bruder, 2014. "Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions," ECON - Working Papers 181, Department of Economics - University of Zurich, revised Dec 2015.
    36. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
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    Cited by:

    1. Sandra L. Haire & Jonathan D. Coop & Carol Miller, 2017. "Characterizing Spatial Neighborhoods of Refugia Following Large Fires in Northern New Mexico USA," Land, MDPI, vol. 6(1), pages 1-24, March.
    2. Neath, Andrew A. & Cavanaugh, Joseph E., 2000. "A regression model selection criterion based on bootstrap bumping for use with resistant fitting," Computational Statistics & Data Analysis, Elsevier, vol. 35(2), pages 155-169, December.
    3. Lan Wang & Runze Li, 2009. "Weighted Wilcoxon-Type Smoothly Clipped Absolute Deviation Method," Biometrics, The International Biometric Society, vol. 65(2), pages 564-571, June.
    4. Çetin, Meral, 2009. "Robust model selection criteria for robust Liu estimator," European Journal of Operational Research, Elsevier, vol. 199(1), pages 21-24, November.
    5. Jinfeng Xu & Zhiliang Ying, 2010. "Simultaneous estimation and variable selection in median regression using Lasso-type penalty," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(3), pages 487-514, June.
    6. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
    7. Arslan, Olcay, 2012. "Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1952-1965.
    8. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, vol. 188(1), pages 40-58.
    9. Halit DURAN, 2018. "Türki̇ye’De Devleti̇n Gi̇ri̇şi̇mci̇li̇k Destekleri̇ Ve Seçi̇lmi̇ş Bazi Deği̇şkenleri̇n Yeni̇ Fi̇rma Doğum Orani Üzeri̇ne Etki̇si̇," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 3(1), pages 68-85.

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