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Zhongjun Qu

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. Zhongjun Qu & Fan Zhuo, 2015. "Likelihood Ratio Based Tests for Markov Regime Switching," Boston University - Department of Economics - Working Papers Series wp2015-003, Boston University - Department of Economics.

    Cited by:

    1. Cavicchioli, Maddalena, 2024. "A matrix unified framework for deriving various impulse responses in Markov switching VAR: Evidence from oil and gas markets," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    2. Fernando Delbianco & Andrés Fioriti & Fernando Tohmé, 2021. "Markov Chains, Eigenvalues and the Stabilityof Economic Growth Processes," Working Papers 88, Red Nacional de Investigadores en Economía (RedNIE).
    3. Karine Constant & Marion Davin & Gilles de Truchis & Benjamin Keddad, 2023. "The European renewable energy sector in calm and turmoil periods: The key role of sovereign risk," CEE-M Working Papers hal-04346858, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
    4. Keddad, Benjamin & Sato, Kiyotaka, 2022. "The influence of the renminbi and its macroeconomic determinants: A new Chinese monetary order in Asia?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    5. Masaru Chiba, 2023. "Robust and efficient specification tests in Markov-switching autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 99-137, April.
    6. Keddad, Benjamin, 2024. "Asian stock market volatility and economic policy uncertainty: The role of world and regional leaders," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    7. Florens Odendahl & Barbara Rossi & Tatevik Sekhposyan, 2021. "Evaluating forecast performance with state dependence," Economics Working Papers 1800, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2025. "The information matrix test for Markov switching autoregressive models with covariate-dependent transition probabilities," Working Papers wp2025_2502, CEMFI.
    9. Kim, Hongjoong & Park, Sungwon & Moon, Kyoung-Sook, 2025. "Markov regime-switching in pricing equity-linked securities: An empirical study for losses in HSCEI-linked products," Finance Research Letters, Elsevier, vol. 76(C).
    10. Barigozzi, Matteo & Massacci, Daniele, 2025. "Modelling large dimensional datasets with Markov switching factor models," Journal of Econometrics, Elsevier, vol. 247(C).
    11. Cavicchioli, Maddalena, 2023. "Impulse response function analysis for Markov switching var models," Economics Letters, Elsevier, vol. 232(C).
    12. Gabriel Rodriguez-Rondon & Jean-Marie Dufour, 2024. "MSTest: An R-Package for Testing Markov Switching Models," Papers 2411.08188, arXiv.org.
    13. Gabriel Rodriguez-Rondon, 2024. "Underlying Core Inflation with Multiple Regimes," Papers 2411.12845, arXiv.org.
    14. Djeutem, Edouard & Dunbar, Geoffrey R., 2022. "Uncovered return parity: Equity returns and currency returns," Journal of International Money and Finance, Elsevier, vol. 128(C).
    15. Donayre, Luiggi & Panovska, Irina, 2021. "Recession-specific recoveries: L’s, U’s and everything in between," Economics Letters, Elsevier, vol. 209(C).
    16. Feng, Shu & Fu, Liang & Ho, Chun-Yu & Alex Ho, Wai-Yip, 2023. "Political stability and credibility of currency board," Journal of International Money and Finance, Elsevier, vol. 137(C).

  2. Zhongjun Qu & Denis Tkachenko, 2015. "Global Identification in DSGE Models Allowing for Indeterminacy," Boston University - Department of Economics - Working Papers Series wp2015-001, Boston University - Department of Economics.

    Cited by:

    1. Khalaf, Lynda & Lin, Zhenjiang, 2021. "Projection-based inference with particle swarm optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    2. Andrzej Kocięcki & Marcin Kolasa, 2022. "A solution to the global identification problem in DSGE models," Working Papers 2022-01, Faculty of Economic Sciences, University of Warsaw.
    3. Jose L. Fillat & STEFANIA GARETTO & Lindsay Oldenski, 2014. "Diversification, Cost Structure, and the Risk Premium of Multinational Corporations," Boston University - Department of Economics - Working Papers Series WP2014-007, Boston University - Department of Economics.
    4. George Alessandria & Horag Choi & Joseph P. Kaboski & Virgiliu Midrigan, 2014. "Microeconomic uncertainty, international trade, and aggregate fluctuations," Working Papers 14-30, Federal Reserve Bank of Philadelphia.
    5. Peter A. Zadrozny, 2022. "Linear Identification of Linear Rational-Expectations Models by Exogenous Variables Reconciles Lucas and Sims," CESifo Working Paper Series 10078, CESifo.
    6. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2019. "Factor-Driven Two-Regime Regression," Working Paper Series no128, Institute of Economic Research, Seoul National University.
    7. Sergey Ivashchenko & Willi Mutschler, 2019. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," CQE Working Papers 8319, Center for Quantitative Economics (CQE), University of Muenster.
    8. Josué Diwambuena & Raquel Fonseca & Stefan Schubert, 2021. "Italian Labour Frictions and Wage Rigidities in an Estimated DSGE," Cahiers de recherche / Working Papers 2105, Chaire de recherche sur les enjeux économiques intergénérationnels / Research Chair in Intergenerational Economics.
    9. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    10. Majid Al-Sadoon & Piotr Zwiernik, 2019. "The identification problem for linear rational expectations models," Economics Working Papers 1669, Department of Economics and Business, Universitat Pompeu Fabra.
    11. Zhongjun Qu & Denis Tkachenko, 2023. "Using arbitrary precision arithmetic to sharpen identification analysis for DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 644-667, June.
    12. Giovanni Nicolo, 2020. "Monetary Policy, Self-Fulfilling Expectations and the U.S. Business Cycle," Finance and Economics Discussion Series 2020-035, Board of Governors of the Federal Reserve System (U.S.).
    13. Timothy Uy, 2015. "Zeros and the Gains from Openness," 2015 Meeting Papers 1158, Society for Economic Dynamics.
    14. Juan Carlos Parra‐Alvarez & Olaf Posch & Mu‐Chun Wang, 2023. "Estimation of Heterogeneous Agent Models: A Likelihood Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 304-330, April.
    15. Giovanni Nicolò, 2025. "US Monetary Policy and Indeterminacy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(2), pages 195-213, March.
    16. Christensen, Bent Jesper & Neri, Luca & Parra-Alvarez, Juan Carlos, 2024. "Estimation of continuous-time linear DSGE models from discrete-time measurements," Journal of Econometrics, Elsevier, vol. 244(2).
    17. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    18. Majid M. Al-Sadoon, 2020. "Regularized Solutions to Linear Rational Expectations Models," Papers 2009.05875, arXiv.org, revised Oct 2020.
    19. Majid M. Al-Sadoon, 2020. "The Spectral Approach to Linear Rational Expectations Models," Papers 2007.13804, arXiv.org, revised Aug 2024.

  3. Zhongjun Qu, 2015. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," Boston University - Department of Economics - Working Papers Series wp2015-002, Boston University - Department of Economics.

    Cited by:

    1. Paul Ho & Thomas A. Lubik & Christian Matthes, 2023. "Averaging Impulse Responses Using Prediction Pools," Working Paper 23-04, Federal Reserve Bank of Richmond.
    2. Bernd Funovits, 2020. "The Dimension of the Set of Causal Solutions of Linear Multivariate Rational Expectations Models," Papers 2002.04369, arXiv.org.
    3. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    4. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.
    5. William Bednar & Nick Pretnar, 2019. "Home Production with Time to Consume," 2019 Meeting Papers 328, Society for Economic Dynamics.

  4. Zhongjun Qu & Jungmo Yoon, 2015. "Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs," Boston University - Department of Economics - Working Papers Series wp2015-009, Boston University - Department of Economics.

    Cited by:

    1. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    2. Federico A. Bugni & Ivan A. Canay & Deborah Kim, 2025. "Testing Conditional Stochastic Dominance at Target Points," Papers 2503.14747, arXiv.org, revised Apr 2025.
    3. Junquera, Álvaro F., 2024. "More money, more effect? Employment effects of job search programs in Veneto," SocArXiv rjshu, Center for Open Science.
    4. Chiang, Harold D. & Sasaki, Yuya, 2019. "Causal inference by quantile regression kink designs," Journal of Econometrics, Elsevier, vol. 210(2), pages 405-433.

  5. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.

    Cited by:

    1. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2016. "A quantile correlated random coefficients panel data model," CeMMAP working papers 34/16, Institute for Fiscal Studies.
    2. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.
    3. Hao, Meiling & Lin, Yuanyuan & Shen, Guohao & Su, Wen, 2023. "Nonparametric inference on smoothed quantile regression process," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    4. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly & Kaspar Wüthrich, 2017. "Generic inference on quantile and quantile effect functions for discrete outcomes," CeMMAP working papers CWP23/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    6. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    7. Arie Beresteanu, 2020. "Quantile Regression with Interval Data," Working Paper 6899, Department of Economics, University of Pittsburgh.
    8. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Iv'an Fern'andez-Val, 2011. "Conditional Quantile Processes based on Series or Many Regressors," Papers 1105.6154, arXiv.org, revised Aug 2018.
    9. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
    10. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.
    11. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    12. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers CWP40/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Liang Chen, 2019. "Nonparametric Quantile Regressions for Panel Data Models with Large T," Papers 1911.01824, arXiv.org, revised Sep 2020.
    14. Feng, Xingdong & Liu, Qiaochu & Wang, Caixing, 2023. "A lack-of-fit test for quantile regression process models," Statistics & Probability Letters, Elsevier, vol. 192(C).
    15. Junquera, Álvaro F., 2024. "More money, more effect? Employment effects of job search programs in Veneto," SocArXiv rjshu, Center for Open Science.
    16. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    17. Chiang, Harold D. & Sasaki, Yuya, 2019. "Causal inference by quantile regression kink designs," Journal of Econometrics, Elsevier, vol. 210(2), pages 405-433.
    18. Shaobo Li & Ben Sherwood, 2025. "Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles," Papers 2505.16019, arXiv.org.
    19. Ferreira, Francisco H. G. & Firpo, Sergio & Galvao, Antonio F., 2017. "Estimation and Inference for Actual and Counterfactual Growth Incidence Curves," IZA Discussion Papers 10473, Institute of Labor Economics (IZA).
    20. Haitian Xie, 2022. "Nonlinear and Nonseparable Structural Functions in Fuzzy Regression Discontinuity Designs," Papers 2204.08168, arXiv.org, revised Jul 2022.
    21. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
    22. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    23. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
    24. Zongwu Cai & Xiyuan Liu, 2020. "A Functional-Coefficient VAR Model for Dynamic Quantiles with Constructing Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202017, University of Kansas, Department of Economics, revised Oct 2020.
    25. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    26. Yijian Huang, 2017. "Restoration of Monotonicity Respecting in Dynamic Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 613-622, April.
    27. Racine, Jeffrey S. & Li, Kevin, 2017. "Nonparametric conditional quantile estimation: A locally weighted quantile kernel approach," Journal of Econometrics, Elsevier, vol. 201(1), pages 72-94.
    28. Yingying DONG & Ying-Ying LEE & Michael GOU, 2019. "Regression Discontinuity Designs with a Continuous Treatment," Discussion papers 19058, Research Institute of Economy, Trade and Industry (RIETI).
    29. Anqi Li & Shiko Maruyama, 2024. "Who suffered most in the pandemic? A distribution regression analysis of happiness in Japan," The Japanese Economic Review, Springer, vol. 75(4), pages 637-690, December.
    30. Zhongjun Qu & Jungmo Yoon, 2015. "Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs," Boston University - Department of Economics - Working Papers Series wp2015-009, Boston University - Department of Economics.
    31. Daniel Hlubinka & Lukáš Kotík & Miroslav Šiman, 2022. "Multivariate quantiles with both overall and directional probability interpretation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1586-1604, December.
    32. Karen X. Yan & Qi Li, 2018. "Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model," JRFM, MDPI, vol. 11(3), pages 1-10, August.

  6. Zhongjun Qu, 2011. "Inference and Speci?cation Testing in DSGE Models with Possible Weak Identification," Boston University - Department of Economics - Working Papers Series WP2011-058, Boston University - Department of Economics.

    Cited by:

    1. Donald W. K. Andrews & Xu Cheng, 2011. "Maximum Likelihood Estimation and Uniform Inference with Sporadic Identification Failure," Cowles Foundation Discussion Papers 1824R, Cowles Foundation for Research in Economics, Yale University, revised Oct 2012.
    2. Donald W.K. Andrews & Xu Cheng, 2011. "GMM Estimation and Uniform Subvector Inference with Possible Identification Failure," Cowles Foundation Discussion Papers 1828, Cowles Foundation for Research in Economics, Yale University.
    3. Anna Mikusheva, 2014. "Estimation of dynamic stochastic general equilibrium models (in Russian)," Quantile, Quantile, issue 12, pages 1-21, February.
    4. Zhongjun Qu & Denis Tkachenko, 2010. "Identification and Frequency Domain QML Estimation of Linearized DSGE Models," Boston University - Department of Economics - Working Papers Series WP2010-053, Boston University - Department of Economics.

  7. Zhongjun Qu & Denis Tkachenko, 2011. "Frequency Domain Analysis of Medium Scale DSGE Models with Application to Smets and Wouters (2007)," Boston University - Department of Economics - Working Papers Series WP2011-060, Boston University - Department of Economics.

    Cited by:

    1. Mutschler, Willi, 2014. "Identification of DSGE Models - A Comparison of Methods and the Effect of Second Order Approximation," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100598, Verein für Socialpolitik / German Economic Association.
    2. Thomas A. Lubik & Christian Matthes & Fabio Verona, 2019. "Assessing U.S. Aggregate Fluctuations Across Time and Frequencies," Working Paper 19-6, Federal Reserve Bank of Richmond.
    3. Alisdair McKay, "undated". "Idiosyncratic risk, insurance, and aggregate consumption dynamics: a likelihood perspective," Boston University - Department of Economics - Working Papers Series 2013-013, Boston University - Department of Economics.
    4. Zhongjun Qu, 2018. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 916-932, December.
    5. Tan, Fei, 2018. "A Frequency-Domain Approach to Dynamic Macroeconomic Models," MPRA Paper 90487, University Library of Munich, Germany.
    6. Lance Kent, 2015. "Relaxing Rational Expectations," Working Papers 159, Department of Economics, College of William and Mary.
    7. Caraiani, Petre, 2015. "Estimating DSGE models across time and frequency," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 33-49.

  8. Zhongjun Qu & Yi-Ting Chen, 2010. "M Tests with a New Normalization Matrix," Boston University - Department of Economics - Working Papers Series WP2010-050, Boston University - Department of Economics.

    Cited by:

    1. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.

  9. Zhongjun Qu, 2010. "A Test Against Spurious Long Memory," Boston University - Department of Economics - Working Papers Series WP2010-051, Boston University - Department of Economics.

    Cited by:

    1. 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.
    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. 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. Sibbertsen, Philipp & Leschinski, Christian & Busch, Marie, 2018. "A multivariate test against spurious long memory," Journal of Econometrics, Elsevier, vol. 203(1), pages 33-49.
    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. Marcel Aloy & Gilles de Truchis, 2015. "Optimal Estimation Strategies for Bivariate Fractional Cointegration Systems and the Co-persistence Analysis of Stock Market Realized Volatilities," Post-Print hal-01410660, HAL.
    7. Dark, Jonathan, 2015. "Futures hedging with Markov switching vector error correction FIEGARCH and FIAPARCH," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 269-285.
    8. Janus, Paweł & Koopman, Siem Jan & Lucas, André, 2014. "Long memory dynamics for multivariate dependence under heavy tails," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 187-206.
    9. Paulo M.M. Rodrigues & Uwe Hassler, 2014. "Persistence in the Banking Industry: Fractional integration and breaks in memory," Working Papers w201406, Banco de Portugal, Economics and Research Department.
    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. Christoph Wegener & Tobias Basse & Philipp Sibbertsen & Duc Khuong Nguyen, 2019. "Liquidity risk and the covered bond market in times of crisis: empirical evidence from Germany," Annals of Operations Research, Springer, vol. 282(1), pages 407-426, November.
    12. Januj Juneja, 2018. "Empirical performance of Gaussian affine dynamic term structure models in the presence of autocorrelation misspecification bias," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 695-715, April.
    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. Philip Bertram & Robinson Kruse & Philipp Sibbertsen, 2013. "Fractional integration versus level shifts: the case of realized asset correlations," Statistical Papers, Springer, vol. 54(4), pages 977-991, November.
    15. Pierre Perron & Yohei Yamamoto, 2011. "Estimating and Testing Multiple Structural Changes in Linear Models Using Band Spectral Regressions," Boston University - Department of Economics - Working Papers Series WP2011-049, Boston University - Department of Economics.
    16. Eduardo Rossi & Paolo Santucci de Magistris, 2009. "Long Memory and Tail dependence in Trading Volume and Volatility," CREATES Research Papers 2009-30, Department of Economics and Business Economics, Aarhus University.
    17. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    18. Robinson Kruse & Christoph Wegener, 2019. "Explosive behaviour and long memory with an application to European bond yield spreads," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 139-153, February.
    19. Andrea Cipollini & Iolanda Lo Cascio & Silvia Muzzioli, 2015. "Financial connectedness among European volatility risk premia," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0058, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    20. OlaOluwa S. Yaya & Xuan Vinh Vo & Ahamuefula E. Ogbonna & Adeolu O. Adewuyi, 2022. "Modelling cryptocurrency high–low prices using fractional cointegrating VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 489-505, January.
    21. Mboya, Mwasi & Sibbertsen, Philipp, 2022. "Optimal Forecasts in the Presence of Discrete Structural Breaks under Long Memory," Hannover Economic Papers (HEP) dp-705, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    22. Jonathan Dark & Xin Gao & Thijs van der Heijden & Federico Nardari, 2022. "Forecasting variance swap payoffs," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2135-2164, December.
    23. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Change-in-Mean Tests in Long-memory Time Series: A Review of Recent Developments," Hannover Economic Papers (HEP) dp-598, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    24. Assaf, Ata & Demir, Ender & Mokni, Khaled, 2024. "Exploring connectedness among cryptocurrency, technology communication, and FinTech through dynamic and fractal analysis," Finance Research Letters, Elsevier, vol. 63(C).
    25. Becker, Janis & Leschinski, Christian & Sibbertsen, Philipp, 2019. "Robust Multivariate Local Whittle Estimation and Spurious Fractional Cointegration," Hannover Economic Papers (HEP) dp-660, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    26. Mohamed Shaker Ahmed & Elie Bouri, 2023. "Long memory and structural breaks of cryptocurrencies trading volume," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(3), pages 469-497, December.
    27. Alexander Boca Saravia & Gabriel Rodríguez, 2022. "Presidential approval in Peru: an empirical analysis using a fractionally cointegrated VAR," Economic Change and Restructuring, Springer, vol. 55(3), pages 1973-2010, August.
    28. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2015. "Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach," CREATES Research Papers 2015-30, Department of Economics and Business Economics, Aarhus University.
    29. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    30. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    31. Marie Busch & Philipp Sibbertsen, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Econometrics, MDPI, vol. 6(1), pages 1-21, March.
    32. Gilles de Truchis & Benjamin Keddad, 2014. "On the risk comovements between the crude oil market and the U.S. dollar exchange rates," Working Papers 2014-383, Department of Research, Ipag Business School.
    33. 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.
    34. Gabriel Rodríguez, 2016. "Modeling Latin-American Stock and Forex Markets Volatility: Empirical Application of a Model with Random Level Shifts and Genuine Long Memory [Modelando la volatilidad de los mercados bursátiles y cam," Documentos de Trabajo / Working Papers 2016-416, Departamento de Economía - Pontificia Universidad Católica del Perú.
    35. Lujia Bai & Weichi Wu, 2021. "Detecting long-range dependence for time-varying linear models," Papers 2110.08089, arXiv.org, revised Mar 2023.
    36. Carina Gerstenberger, 2021. "Robust discrimination between long‐range dependence and a change in mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 34-62, January.
    37. 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.
    38. Charfeddine, Lanouar & Maouchi, Youcef, 2019. "Are shocks on the returns and volatility of cryptocurrencies really persistent?," Finance Research Letters, Elsevier, vol. 28(C), pages 423-430.
    39. Cipollini, Andrea & Lo Cascio, Iolanda & Muzzioli, Silvia, 2018. "Risk aversion connectedness in five European countries," Economic Modelling, Elsevier, vol. 71(C), pages 68-79.
    40. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
    41. Dark, Jonathan, 2018. "Multivariate models with long memory dependence in conditional correlation and volatility," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 162-180.
    42. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    43. Renzo Pardo Figueroa & Gabriel Rodríguez, 2014. "Distinguishing between True and Spurious Long Memory in the Volatility of Stock Market Returns in Latin America," Documentos de Trabajo / Working Papers 2014-395, Departamento de Economía - Pontificia Universidad Católica del Perú.
    44. Luis Alberiko & OlaOluwa S. Yaya & Olarenwaju I. Shittu, 2015. "Fractional integration and asymmetric volatility in european, asian and american bull and bear markets. Applications to high frequency stock data," NCID Working Papers 07/2015, Navarra Center for International Development, University of Navarra.
    45. Andrea Cipollini & Iolanda Lo Cascio & Silvia Muzzioli, 2014. "Volatility risk premia and financial connectedness," Department of Economics 0047, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    46. 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.
    47. Andrea Cipollini & Iolanda Lo Cascio & Silvia Muzzioli, 2014. "Volatility risk premia and financial connectedness," Center for Economic Research (RECent) 109, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    48. Leschinski, Christian & Sibbertsen, Philipp, 2017. "Origins of Spurious Long Memory," Hannover Economic Papers (HEP) dp-595, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    49. Lovcha, Yuliya & Pérez Laborda, Àlex, 2018. "Volatility Spillovers in a Long-Memory VAR: an Application to Energy Futures Returns," Working Papers 2072/307362, Universitat Rovira i Virgili, Department of Economics.
    50. 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.
    51. 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.
    52. Kruse, Robinson, 2015. "A modified test against spurious long memory," Economics Letters, Elsevier, vol. 135(C), pages 34-38.
    53. 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.
    54. 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.
    55. Dark, Jonathan, 2024. "An adaptive long memory conditional correlation model," Journal of Empirical Finance, Elsevier, vol. 75(C).
    56. Alia Afzal & Philipp Sibbertsen, 2023. "Long Memory, Spurious Memory: Persistence in Range-Based Volatility of Exchange Rates," Open Economies Review, Springer, vol. 34(4), pages 789-811, September.
    57. Rinke, Saskia & Busch, Marie & Leschinski, Christian, 2017. "Long Memory, Breaks, and Trends: On the Sources of Persistence in Inflation Rates," Hannover Economic Papers (HEP) dp-584, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    58. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.
    59. Arturo Leccadito & Omar Rachedi & Giovanni Urga, 2015. "True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 452-479, April.
    60. Niels Haldrup & Robinson Kruse, 2014. "Discriminating between fractional integration and spurious long memory," CREATES Research Papers 2014-19, Department of Economics and Business Economics, Aarhus University.
    61. Gabriel Rodríguez & Junior A. Ojeda Cunya & José Carlos Gonzáles Tanaka, 2019. "An empirical note about estimation and forecasting Latin American Forex returns volatility: the role of long memory and random level shifts components," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 18(2), pages 107-123, June.
    62. Matei Demetrescu & Mehdi Hosseinkouchack, 2022. "Autoregressive spectral estimates under ignored changes in the mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 329-340, March.
    63. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    64. Dennis Alvaro & Ángel Guillén & Gabriel Rodríguez, 2017. "Modelling the volatility of commodities prices using a stochastic volatility model with random level shifts," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 153(1), pages 71-103, February.
    65. Francisco Estrada & Pierre Perron, "undated". "Detection and attribution of climate change through econometric methods," Boston University - Department of Economics - Working Papers Series 2013-015, Boston University - Department of Economics.
    66. Jiawen Xu & Pierre Perron, 2013. "Robust testing of time trend and mean with unknown integration order errors Frequency (and Other) Contaminations," Boston University - Department of Economics - Working Papers Series 2013-006, Boston University - Department of Economics.
    67. Ye Li & Pierre Perron & Jiawen Xu, 2017. "Modelling exchange rate volatility with random level shifts," Applied Economics, Taylor & Francis Journals, vol. 49(26), pages 2579-2589, June.
    68. Chen, Cathy Yi-Hsuan & Chiang, Thomas C. & Härdle, Wolfgang Karl, 2016. "Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries," SFB 649 Discussion Papers 2016-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    69. Leschinski, Christian & Sibbertsen, Philipp, 2018. "The Periodogram of Spurious Long-Memory Processes," Hannover Economic Papers (HEP) dp-632, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    70. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    71. Daeyun Kang & Doojin Ryu & Robert I. Webb, 2025. "Bitcoin as a financial asset: a survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-28, December.
    72. M. A. Limam & V. Terraza & M. Terraza, 2017. "Hedge Fund Return Dynamics: Long Memory and Regime Switching," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 8(4), pages 148-166, October.
    73. Juneja, Januj A., 2016. "Financial crises and estimation bias in international bond markets," Research in International Business and Finance, Elsevier, vol. 38(C), pages 593-607.
    74. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "Crude oil price behaviour before and after military conflicts and geopolitical events," Energy, Elsevier, vol. 120(C), pages 79-91.
    75. 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.
    76. Iacone Fabrizio & Leybourne Stephen J. & Robert Taylor A.M., 2017. "Testing for a Change in Mean under Fractional Integration," Journal of Time Series Econometrics, De Gruyter, vol. 9(1), pages 1-8, January.
    77. Chen, Cathy Yi-Hsuan & Chiang, Thomas C. & Härdle, Wolfgang Karl, 2018. "Downside risk and stock returns in the G7 countries: An empirical analysis of their long-run and short-run dynamics," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 21-32.
    78. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
    79. 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.
    80. Kellard, Neil M. & Jiang, Ying & Wohar, Mark, 2015. "Spurious long memory, uncommon breaks and the implied–realized volatility puzzle," Journal of International Money and Finance, Elsevier, vol. 56(C), pages 36-54.
    81. Al-Shboul, Mohammad & Anwar, Sajid, 2016. "Fractional integration in daily stock market indices at Jordan's Amman stock exchange," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 16-37.
    82. 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.
    83. Andrés Herrera Aramburú & Gabriel Rodríguez, 2016. "Volatility of stock market and exchange rate returns in Peru: Long memory or short memory with level shifts?," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 45-66.
    84. Dooruj Rambaccussing & Murat Mazibas, 2020. "True versus Spurious Long Memory in Cryptocurrencies," JRFM, MDPI, vol. 13(9), pages 1-11, August.
    85. Jorge Barrientos Marin & Laura Marquez Marulanda & Fernando Villada Duque, 2023. "Analyzing Electricity Demand in Colombia: A Functional Time Series Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 75-84, January.
    86. Klaus Grobys & Sami Vähämaa, 2020. "Another look at value and momentum: volatility spillovers," Review of Quantitative Finance and Accounting, Springer, vol. 55(4), pages 1459-1479, November.
    87. 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.
    88. Berna Kirkulak-Uludag & Zorikto Lkhamazhapov, 2017. "Volatility Dynamics of Precious Metals: Evidence from Russia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(4), pages 300-317, August.
    89. Tan, Zhengxun & Xiao, Binuo & Huang, Yilong & Zhou, Li, 2021. "Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).

  10. Zhongjun Qu & Tatsushi Oka, 2010. "Estimating structural changes in regression quantiles," Boston University - Department of Economics - Working Papers Series WP2010-052, Boston University - Department of Economics.

    Cited by:

    1. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    2. Tillmann, Peter & Wolters, Maik Hendrik, 2012. "The changing dynamics of US inflation persistence: A quantile regression approach," IMFS Working Paper Series 60, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    3. Zhanfeng Wang & Wenxin Liu & Yuanyuan Lin, 2015. "A change-point problem in relative error-based regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 835-856, December.
    4. Michael Pfarrhofer, 2021. "Modeling tail risks of inflation using unobserved component quantile regressions," Papers 2103.03632, arXiv.org, revised Oct 2021.
    5. Christian Bauer & Sebastian Weber, 2016. "The Efficiency of Monetary Policy when Guiding Inflation Expectations," Research Papers in Economics 2016-14, University of Trier, Department of Economics.
    6. Mayer, Alexander & Wied, Dominik & Troster, Victor, 2025. "Quantile Granger causality in the presence of instability," Journal of Econometrics, Elsevier, vol. 249(PB).
    7. Weichi Wu & Zhou Zhou, 2017. "Nonparametric Inference for Time-Varying Coefficient Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 98-109, January.
    8. Rangan Gupta & Charl Jooste & Omid Ranjbar, 2017. "South Africa’s inflation persistence: a quantile regression framework," Economic Change and Restructuring, Springer, vol. 50(4), pages 367-386, November.
    9. Ghysels, Eric & Liu, Hanwei, 2017. "Downside Risk in the Chinese Stock Market - Has it Fundamentally Changed?," CEPR Discussion Papers 12180, C.E.P.R. Discussion Papers.
    10. Tatsushi Oka & Pierre Perron, 2018. "Testing for common breaks in a multiple equations system," Monash Econometrics and Business Statistics Working Papers 3/18, Monash University, Department of Econometrics and Business Statistics.
    11. Qu, Zhongjun & Yoon, Jungmo, 2015. "Nonparametric estimation and inference on conditional quantile processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 1-19.
    12. Yannick Hoga & Christian Schulz, 2025. "Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series," Papers 2502.10065, arXiv.org, revised Jun 2025.
    13. Sebastiano Manzan & Dawit Zerom, 2015. "Asymmetric Quantile Persistence and Predictability: the Case of US Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(2), pages 297-318, April.
    14. Alexander Aue & Rex C. Y. Cheung & Thomas C. M. Lee & Ming Zhong, 2014. "Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1241-1256, September.
    15. Yan-Yu Chiou & Mei-Yuan Chen & Jau-er Chen, 2017. "Nonparametric Regression with Multiple Thresholds: Estimation and Inference," Papers 1705.09418, arXiv.org, revised Feb 2018.
    16. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy & Wohar, Mark E., 2018. "Do house prices hedge inflation in the US? A quantile cointegration approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 15-26.
    17. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    18. Yannick Hoga, 2024. "Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions," Papers 2410.05861, arXiv.org.
    19. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Modelling the COVID‐19 infection trajectory: A piecewise linear quantile trend model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1589-1607, November.
    20. Ji, Qiang & Zhang, Dayong & Zhao, Yuqian, 2020. "Searching for safe-haven assets during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 71(C).
    21. Chiang, Harold D. & Sasaki, Yuya, 2019. "Causal inference by quantile regression kink designs," Journal of Econometrics, Elsevier, vol. 210(2), pages 405-433.
    22. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Jiang, Zhao and Shao's reply to the Discussion of ‘The First Discussion Meeting on Statistical Aspects of the Covid‐19 Pandemic’," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1849-1854, October.
    23. Yunmi Kim & Lijuan Huo & Tae-Hwan Kim, 2020. "Dealing with Markov-Switching Parameters in Quantile Regression Models," Working papers 2020rwp-166, Yonsei University, Yonsei Economics Research Institute.
    24. Yu, Ping, 2015. "Adaptive estimation of the threshold point in threshold regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 83-100.
    25. Zhongxin Ni & Xing Lu & Wenjun Xue, 2021. "Does the belt and road initiative resolve the steel overcapacity in China? Evidence from a dynamic model averaging approach," Empirical Economics, Springer, vol. 61(1), pages 279-307, July.
    26. Emanuele Russo & Neil Foster-McGregor & Bart Verpagen, 2019. "Characterizing growth instability: new evidence on unit roots and structural breaks in long run time series," LEM Papers Series 2019/29, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    27. Chuang Wan & Wei Zhong & Wenyang Zhang & Changliang Zou, 2023. "Multikink quantile regression for longitudinal data with application to progesterone data analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 747-760, June.
    28. Fenghua Wen & Kaiyan Weng & Wei-Xing Zhou, 2020. "Measuring the contribution of Chinese financial institutions to systemic risk: an extended asymmetric CoVaR approach," Risk Management, Palgrave Macmillan, vol. 22(4), pages 310-337, December.
    29. Zhou, Mi & Wang, Huixia Judy & Tang, Yanlin, 2015. "Sequential change point detection in linear quantile regression models," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 98-103.
    30. Uribe, Jorge M. & Chuliá, Helena & Guillén, Montserrat, 2017. "Uncertainty, systemic shocks and the global banking sector: Has the crisis modified their relationship?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 52-68.
    31. Jean-Paul Chavas & Salvatore Falco, 2017. "Resilience, Weather and Dynamic Adjustments in Agroecosystems: The Case of Wheat Yield in England," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(2), pages 297-320, June.
    32. Emanuele Russo & Neil Foster-McGregor, 2022. "Characterizing growth instability: new evidence on unit roots and structural breaks in countries’ long run trajectories," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 713-756, April.
    33. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
    34. Alessandro Casini & Pierre Perron, 2018. "Generalized Laplace Inference in Multiple Change-Points Models," Papers 1803.10871, arXiv.org, revised Jan 2021.
    35. Tolga Omay & Rangan Gupta & Giovanni Bonaccolto, 2015. "The US Real GNP is Trend-Stationary After All," Working Papers 201581, University of Pretoria, Department of Economics.
    36. Gabriel Montes-Rojas & Zacharias Psaradakis & Martín Sola, 2024. "On Regime Separation in Markov-Switching Quantile Regressions," Department of Economics Working Papers 2024_05, Universidad Torcuato Di Tella.
    37. Wolters, Maik Hendrik, 2010. "Estimating Monetary Policy Reaction Functions Using Quantile Regressions," MPRA Paper 23857, University Library of Munich, Germany.
    38. Marilena Furno, 2021. "Cointegration tests at the quantiles," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1087-1100, January.
    39. Marilena Furno, 2012. "Tests for structural break in quantile regressions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(4), pages 493-515, October.
    40. Sangyeol Lee & Chang Kyeom Kim, 2024. "Test for conditional quantile change in general conditional heteroscedastic time series models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(2), pages 333-359, April.
    41. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
    42. Kuriyama Nina, 2016. "Testing cointegration in quantile regressions with an application to the term structure of interest rates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 107-121, April.
    43. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    44. Sahbi FARHANI, 2012. "Tests of Parameters Instability: Theoretical Study and Empirical Analysis on Two Types of Models (ARMA Model and Market Model)," International Journal of Economics and Financial Issues, Econjournals, vol. 2(3), pages 246-266.
    45. Rangan Gupta & Charl Jooste & Omid Ranjbar, 2015. "The Changing Dynamics of South Africa's Inflation Persistence: Evidence from a Quantile Regression Framework," Working Papers 201563, University of Pretoria, Department of Economics.
    46. Zeng, Zijian & Li, Meng, 2021. "Bayesian median autoregression for robust time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1000-1010.
    47. Chiou, Yan-Yu & Chen, Mei-Yuan & Chen, Jau-er, 2018. "Nonparametric regression with multiple thresholds: Estimation and inference," Journal of Econometrics, Elsevier, vol. 206(2), pages 472-514.
    48. Zhang, Yingying & Wang, Huixia Judy & Zhu, Zhongyi, 2019. "Quantile-regression-based clustering for panel data," Journal of Econometrics, Elsevier, vol. 213(1), pages 54-67.
    49. Wen-Yi Chen & Tsangyao Chang & Yu-Hui Lin, 2018. "Investigating the Persistence of Suicide in the United States: Evidence from the Quantile Unit Root Test," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 813-833, January.

  11. Zhongjun Qu & Denis Tkachenko, 2010. "Identification and Frequency Domain QML Estimation of Linearized DSGE Models," Boston University - Department of Economics - Working Papers Series WP2010-053, Boston University - Department of Economics.

    Cited by:

    1. Mark A. Wynne, 2011. "Dynamic Stochastic General-Equilibrium Modeling: 10th Annual Advances in Econometrics Conference," Annual Report, Globalization and Monetary Policy Institute, Federal Reserve Bank of Dallas, pages 39-42.
    2. Zhongjun Qu & Denis Tkachenko, 2011. "Frequency Domain Analysis of Medium Scale DSGE Models with Application to Smets and Wouters (2007)," Boston University - Department of Economics - Working Papers Series WP2011-060, Boston University - Department of Economics.
    3. Canova, Fabio & Ferroni, Filippo & Matthes, Christian, 2013. "Choosing the variables to estimate singular DSGE models," CEPR Discussion Papers 9381, C.E.P.R. Discussion Papers.
    4. Zhongjun Qu, 2011. "Inference and Speci?cation Testing in DSGE Models with Possible Weak Identification," Boston University - Department of Economics - Working Papers Series WP2011-058, Boston University - Department of Economics.

  12. 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.

    Cited by:

    1. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    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. 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. 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.
    5. Sibbertsen, Philipp & Leschinski, Christian & Busch, Marie, 2018. "A multivariate test against spurious long memory," Journal of Econometrics, Elsevier, vol. 203(1), pages 33-49.
    6. Massimiliano Giacalone & Demetrio Panarello, 2022. "A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments," Mathematics, MDPI, vol. 10(5), pages 1-21, February.
    7. Xu, Jiawen & Perron, Pierre, 2014. "Forecasting return volatility: Level shifts with varying jump probability and mean reversion," International Journal of Forecasting, Elsevier, vol. 30(3), pages 449-463.
    8. Vasileios Siakoulis & Ioannis Venetis, 2015. "On inter-arrival times of bond market extreme events. An application to seven European markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(4), pages 717-741, October.
    9. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    10. Nima Nonejad, 2019. "Modeling Persistence and Parameter Instability in Historical Crude Oil Price Data Using a Gibbs Sampling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1687-1710, April.
    11. Kambouroudis, Dimos S. & McMillan, David G., 2015. "Is there an ideal in-sample length for forecasting volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 114-137.
    12. Aloy, Marcel & Boutahar, Mohamed & Gente, Karine & Péguin-Feissolle, Anne, 2011. "Purchasing power parity and the long memory properties of real exchange rates: Does one size fit all?," Economic Modelling, Elsevier, vol. 28(3), pages 1279-1290, May.
    13. Ewing, Bradley T. & Malik, Farooq, 2016. "Volatility spillovers between oil prices and the stock market under structural breaks," Global Finance Journal, Elsevier, vol. 29(C), pages 12-23.
    14. Paulo M.M. Rodrigues & Uwe Hassler, 2014. "Persistence in the Banking Industry: Fractional integration and breaks in memory," Working Papers w201406, Banco de Portugal, Economics and Research Department.
    15. 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.
    16. 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.
    17. 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.
    18. Mohitosh Kejriwal & Xuewen Yu & Pierre Perron, 2020. "Bootstrap Procedures for Detecting Multiple Persistence Shifts in Heteroskedastic Time Series," Boston University - Department of Economics - Working Papers Series WP2020-009, Boston University - Department of Economics.
    19. Pierre Perron & Yohei Yamamoto, 2011. "Estimating and Testing Multiple Structural Changes in Linear Models Using Band Spectral Regressions," Boston University - Department of Economics - Working Papers Series WP2011-049, Boston University - Department of Economics.
    20. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    21. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    22. Abanto-Valle, Carlos A. & Rodríguez, Gabriel & Garrafa-Aragón, Hernán B., 2021. "Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 272-286.
    23. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    24. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    25. Susanne M. Schennach, 2013. "Long memory via networking," CeMMAP working papers CWP13/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    26. Xu, Ke-Li, 2013. "Powerful tests for structural changes in volatility," Journal of Econometrics, Elsevier, vol. 173(1), pages 126-142.
    27. Junior Ojeda & Gabriel Rodriguez, 2014. "An Application of a Random Level Shifts Model to the Volatility of Peruvian Stock and Exchange Rates Returns," Documentos de Trabajo / Working Papers 2014-383, Departamento de Economía - Pontificia Universidad Católica del Perú.
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    87. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    88. Dennis Alvaro & Ángel Guillén & Gabriel Rodríguez, 2017. "Modelling the volatility of commodities prices using a stochastic volatility model with random level shifts," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 153(1), pages 71-103, February.
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  13. 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.

    Cited by:

    1. 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.
    2. 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.
    3. 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.

  14. 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.

    Cited by:

    1. Xu, Jiawen & Perron, Pierre, 2014. "Forecasting return volatility: Level shifts with varying jump probability and mean reversion," International Journal of Forecasting, Elsevier, vol. 30(3), pages 449-463.
    2. Nima Nonejad, 2019. "Modeling Persistence and Parameter Instability in Historical Crude Oil Price Data Using a Gibbs Sampling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1687-1710, April.
    3. 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.
    4. Pierre Perron & Yohei Yamamoto, 2011. "Estimating and Testing Multiple Structural Changes in Linear Models Using Band Spectral Regressions," Boston University - Department of Economics - Working Papers Series WP2011-049, Boston University - Department of Economics.
    5. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    6. Chevillon, G. & Hecq, A.W. & Laurent, S.F.J.A., 2015. "Long memory through marginalization of large systems and hidden cross-section dependence," Research Memorandum 014, Maastricht University, Graduate School of Business and Economics (GSBE).
    7. Susanne M. Schennach, 2013. "Long memory via networking," CeMMAP working papers CWP13/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Guillaume Chevillon & Alain Hecq & Sébastien Laurent, 2018. "Generating Univariate Fractional Integration within a Large VAR(1)," Working Papers halshs-01944588, HAL.
    9. 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.
    10. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    11. Razvan Pascalau & Christian Thomann & Greg N. Gregoriou, 2011. "Unconditional Mean, Volatility, and the FOURIER-GARCH Representation," Palgrave Macmillan Books, in: Greg N. Gregoriou & Razvan Pascalau (ed.), Financial Econometrics Modeling: Derivatives Pricing, Hedge Funds and Term Structure Models, chapter 5, pages 90-106, Palgrave Macmillan.
    12. Chevillon, Guillaume & Mavroeidis, Sophocles, 2017. "Learning can generate long memory," Journal of Econometrics, Elsevier, vol. 198(1), pages 1-9.
    13. 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.
    14. Gabriel Rodríguez, 2016. "Modeling Latin-American Stock and Forex Markets Volatility: Empirical Application of a Model with Random Level Shifts and Genuine Long Memory [Modelando la volatilidad de los mercados bursátiles y cam," Documentos de Trabajo / Working Papers 2016-416, Departamento de Economía - Pontificia Universidad Católica del Perú.
    15. 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.
    16. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    17. Chevillon, Guillaume, 2009. "Multi-step forecasting in emerging economies: An investigation of the South African GDP," International Journal of Forecasting, Elsevier, vol. 25(3), pages 602-628, July.
    18. Renzo Pardo Figueroa & Gabriel Rodríguez, 2014. "Distinguishing between True and Spurious Long Memory in the Volatility of Stock Market Returns in Latin America," Documentos de Trabajo / Working Papers 2014-395, Departamento de Economía - Pontificia Universidad Católica del Perú.
    19. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
    20. Russell Davidson, 2010. "An Agnostic Look at Bayesian Statistics and Econometrics," Working Papers halshs-00541163, HAL.
    21. Guillaume Chevillon, 2013. "Robust Cointegration Testing in the Presence of Weak Trends, with an Application to the Human Origin of Global Warming," Working Papers hal-00914830, HAL.
    22. Peter S. Sephton, 2009. "Fractional integration in agricultural futures price volatilities revisited," Agricultural Economics, International Association of Agricultural Economists, vol. 40(1), pages 103-111, January.
    23. Chevillon, Guillaume & Mavroeidis, Sophocles, 2011. "Learning generates Long Memory," ESSEC Working Papers WP1113, ESSEC Research Center, ESSEC Business School.
    24. Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013. "Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model," Working Papers 201357, University of Pretoria, Department of Economics.
    25. 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.
    26. Abdul Aziz Karia & Imbarine Bujang & Ismail Ahmad, 2013. "Fractionally integrated ARMA for crude palm oil prices prediction: case of potentially overdifference," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2735-2748, December.
    27. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Post-Print hal-01982032, HAL.
    28. Mohamed El Hedi Arouri & Shawkat Hammoudeh & Amine Lahiani & Duc Khuong Nguyen, 2013. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," Working Papers hal-00798033, HAL.
    29. Matei Demetrescu & Mehdi Hosseinkouchack, 2022. "Autoregressive spectral estimates under ignored changes in the mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 329-340, March.
    30. Dennis Alvaro & Ángel Guillén & Gabriel Rodríguez, 2017. "Modelling the volatility of commodities prices using a stochastic volatility model with random level shifts," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 153(1), pages 71-103, February.
    31. 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.
    32. 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.
    33. Juan Hoyo & Guillermo Llorente & Carlos Rivero, 2020. "A Testing Procedure for Constant Parameters in Stochastic Volatility Models," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 163-186, June.
    34. Adnen Ben Nasr & Mohamed Boutahar & Abdelwahed Trabelsi, 2010. "Fractionally integrated time varying GARCH model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(3), pages 399-430, August.
    35. Andrés Herrera Aramburú & Gabriel Rodríguez, 2016. "Volatility of stock market and exchange rate returns in Peru: Long memory or short memory with level shifts?," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 45-66.

  15. Pierre Perron & Zhongjun Qu, 2006. "An Analytical Evaluation of the Log-periodogram Estimate in the Presence of Level Shifts and its Implications for Stock Returns Volatility," Boston University - Department of Economics - Working Papers Series WP2006-016, Boston University - Department of Economics.

    Cited by:

    1. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    2. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
    3. McMillan, David G. & Ruiz, Isabel, 2009. "Volatility persistence, long memory and time-varying unconditional mean: Evidence from 10 equity indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 578-595, May.
    4. McMillan, David G., 2009. "The confusing time-series behaviour of real exchange rates: Are asymmetries important?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(4), pages 692-711, October.
    5. David G. McMillan, 2010. "Level‐shifts and non‐linearity in US financial ratios," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 9(2), pages 189-207, May.
    6. Umar Muhammad Gummi & Yang Rong & Utiya Bello & Abdulhamid Sillah Umar & Asiya Mu'azu, 2021. "On the Analysis of Food and Oil Markets in Nigeria: What Prices Tell Us from Asymmetric and Partial Structural Change Modeling?," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 52-64.
    7. Tan, Zhengxun & Xiao, Binuo & Huang, Yilong & Zhou, Li, 2021. "Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).

  16. Zhongjun Qu & Pierre Perron, 2006. "A Modified Information Criterion for Cointegration Tests based on a VAR Approximation," Boston University - Department of Economics - Working Papers Series WP2006-011, Boston University - Department of Economics.

    Cited by:

    1. Yan Qian & Zijun Wang, 2021. "A model selection approach to jointly testing for structural breaks and cointegration with application to the Eurocurrency interest rates market," Empirical Economics, Springer, vol. 61(2), pages 799-825, August.
    2. Antonia Arsova, 2019. "Exchange rate pass-through to import prices in Europe: A panel cointegration approach," Working Paper Series in Economics 384, University of Lüneburg, Institute of Economics.
    3. Kascha, Christian & Trenkler, Carsten, 2011. "Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1008-1017, February.
    4. Giorgio Mattei & Barbara Pistoresi, 2019. "Unemployment and suicide in Italy: evidence of a long-run association mitigated by public unemployment spending," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(4), pages 569-577, June.
    5. Marco Morales, 2014. "Cointegration testing under structural change: reducing size distortions and improving power of residual based tests," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 265-282, June.
    6. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2024. "Inference in Heavy-Tailed Nonstationary Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 565-581, January.
    7. Antonia Arsova, 2021. "Exchange rate pass-through to import prices in Europe: a panel cointegration approach," Empirical Economics, Springer, vol. 61(1), pages 61-100, July.
    8. Harris, D & Leybourne, SJ & Taylor, AMR, 2016. "Tests of the Co-integration Rank in VAR Models in the Presence of a Possible Break in Trend at an Unknown Point," Essex Finance Centre Working Papers 15847, University of Essex, Essex Business School.
    9. El-Shagi, Makram, 2010. "An Evolutionary Algorithm for the Estimation of Threshold Vector Error Correction Models," IWH Discussion Papers 1/2010, Halle Institute for Economic Research (IWH).
    10. Bauer, Dietmar & Wagner, Martin, 2009. "Using subspace algorithm cointegration analysis: Simulation performance and application to the term structure," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1954-1973, April.
    11. Tu, Yundong & Yi, Yanping, 2017. "Forecasting cointegrated nonstationary time series with time-varying variance," Journal of Econometrics, Elsevier, vol. 196(1), pages 83-98.
    12. Arsova, Antonia & Karaman Örsal, Deniz Dilan, 2021. "A panel cointegrating rank test with structural breaks and cross-sectional dependence," Econometrics and Statistics, Elsevier, vol. 17(C), pages 107-129.
    13. Carrion-i-Silvestre Josep Lluis & Surdeanu Laura, 2011. "Panel Cointegration Rank Testing with Cross-Section Dependence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-43, September.
    14. Sebastian Fossati, 2011. "Covariate Unit Root Tests with Good Size and Power," Working Papers 2011-4, University of Alberta, Department of Economics.

  17. Pierre Perron & Zhongjun Qu, 2006. "A Simple Modification to Improve the Finite Sample Properties of Ng and Perron’s Unit Root Tests," Boston University - Department of Economics - Working Papers Series WP2006-010, Boston University - Department of Economics.

    Cited by:

    1. Del Barrio Castro, T & Rodrigues, PMM & Taylor, AMR, 2015. "Semi-Parametric Seasonal Unit Root Tests," Essex Finance Centre Working Papers 16807, University of Essex, Essex Business School.
    2. Thilo Reinschlussel & Martin C. Arnold, 2024. "Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso," Papers 2402.16580, arXiv.org, revised Jul 2024.
    3. Herzer, Dierk, 2020. "Semi-endogenous versus Schumpeterian growth models: a critical review of the literature and new evidence," MPRA Paper 98022, University Library of Munich, Germany.
    4. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2013. "Testing for unit roots in the possible presence of multiple trend breaks using minimum Dickey–Fuller statistics," Journal of Econometrics, Elsevier, vol. 177(2), pages 265-284.
    5. Skrobotov Anton, 2018. "On Bootstrap Implementation of Likelihood Ratio Test for a Unit Root," Working Papers wpaper-2018-302, Gaidar Institute for Economic Policy, revised 2018.
    6. Syed Abul Basher & Alfred Haug & Perry Sadorsky, 2010. "Oil Prices, Exchange Rates and Emerging Stock Markets," Working Papers 1014, University of Otago, Department of Economics, revised Sep 2010.
    7. Samuel Brien & Michael Jansson & Morten Ørregaard Nielsen, 2022. "Nearly Efficient Likelihood Ratio Tests of a Unit Root in an Autoregressive Model of Arbitrary Order," Working Paper 1429, Economics Department, Queen's University.
    8. Allin Cottrell, 2021. "Response surfaces for DF-GLS p-values," gretl working papers 8, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    9. Giuseppe Cavaliere & David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2009. "Testing for unit roots in the presence of a possible break in trend and non-stationary volatility," Discussion Papers 09/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    10. Wojciech Charemza & Svetlana Makarova & Imran Shah, 2015. "Making the most of high inflation," Applied Economics, Taylor & Francis Journals, vol. 47(34-35), pages 3723-3739, July.
    11. A., Rjumohan, 2019. "Integration between Economic Growth and Financial Development in India: An Analysis," MPRA Paper 101856, University Library of Munich, Germany.
    12. Smeekes, S., 2011. "Bootstrap sequential tests to determine the stationary units in a panel," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    13. Ali, Amjad, 2022. "Determining Pakistan's Financial Dependency: The Role of Financial Globalization and Corruption," MPRA Paper 116097, University Library of Munich, Germany.
    14. Imran H. Shah & Ian Corrick & Abdul Saboor, 2018. "How should Central Banks Respond to Non-neutral Inflation Expectations?," Open Economies Review, Springer, vol. 29(2), pages 321-351, April.
    15. Paulo M. M. Rodrigues, 2013. "Recursive adjustment, unit root tests and structural breaks," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 62-82, January.
    16. Stephan Smeekes & A. M. Robert Taylor, 2010. "Bootstrap union tests for unit roots in the presence of nonstationary volatility," Discussion Papers 10/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    17. Chowdhury, Rosen & Cook, Steve & Watson, Duncan, 2023. "Reconsidering the relationship between health and income in the UK," Social Science & Medicine, Elsevier, vol. 332(C).
    18. Amélie Charles & Chew Lian Chua & Olivier Darné & Sandy Suardi, 2020. "On the Pernicious Effects of Oil Price Uncertainty on U.S. Real Economic Activities," Post-Print hal-03040689, HAL.
    19. 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.
    20. Haug Alfred A & Beyer Andreas & Dewald William, 2011. "Structural Breaks and the Fisher Effect," The B.E. Journal of Macroeconomics, De Gruyter, vol. 11(1), pages 1-31, May.
    21. Josep Lluís Carrion-I-Silvestre & María Dolores Gadea, 2016. "Bounds, Breaks and Unit Root Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 165-181, March.
    22. Omid Zamani & Thomas Bittmann & Jens‐Peter Loy, 2024. "Does the internet bring food prices closer together? Exploring search engine query data in Iran," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(2), pages 688-715, June.
    23. Stephan Smeekes, 2013. "Detrending Bootstrap Unit Root Tests," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 869-891, November.
    24. Ching-Chuan Tsong, 2010. "Are Real Exchange Rates Mean Reverting in Developing Economies in Asia? A Covariate Stationarity Approach," International Economic Journal, Taylor & Francis Journals, vol. 24(3), pages 397-412.
    25. Saeid Mahdavi & Joakim Westerlund, 2017. "Are state–local government expenditures converging? New evidence based on sequential unit root tests," Empirical Economics, Springer, vol. 53(2), pages 373-403, September.
    26. Umit BULUT, 2015. "The Interest Rate Corridor as a Macroprudential Tool to Mitigate Rapid Growth in Credits: Evidence from Turkey," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(605), W), pages 133-144, Winter.
    27. Nikita D. Fokin & Ekaterina V. Malikova & Andrey V. Polbin, 2024. "Time-varying parameters error correction model for real ruble exchange rate and oil prices: What has changed due to capital control and sanctions?," Russian Journal of Economics, ARPHA Platform, vol. 10(1), pages 20-33, March.
    28. Josep Lluís Carrion-i-Silvestre & Dukpa Kim & Pierre Perron, 2007. "GLS-based unit root tests with multiple structural breaks both under the null and the alternative hypotheses," Boston University - Department of Economics - Working Papers Series wp2008-019, Boston University - Department of Economics.
    29. Jump, Robert & Mendieta-Muñoz, Ivan, 2016. "Wage Led Aggregate Demand in the United Kingdom," MPRA Paper 69630, University Library of Munich, Germany.
    30. Paraskevi Salamaliki & Ioannis Venetis, 2014. "Smooth transition trends and labor force participation rates in the United States," Empirical Economics, Springer, vol. 46(2), pages 629-652, March.
    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. Morten Ø. Nielsen, 2008. "A Powerful Tuning Parameter Free Test Of The Autoregressive Unit Root Hypothesis," Working Paper 1175, Economics Department, Queen's University.
    33. Pierre Perron & Eduardo Zorita & Iliyan Georgiev & Paulo M. M. Rodrigues & A. M. Robert Taylor, 2017. "Unit Root Tests and Heavy-Tailed Innovations," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 733-768, September.
    34. Hugo Ferrer-Pérez & María-Isabel Ayuda & Antonio Aznar, 2019. "Improving the Performance of a Long-Run Variance Ratio Test for a Unit Root," The Japanese Economic Review, Springer, vol. 70(2), pages 258-274, June.
    35. Diamandis, Panayiotis F., 2009. "International stock market linkages: Evidence from Latin America," Global Finance Journal, Elsevier, vol. 20(1), pages 13-30.
    36. Ivan Mendieta-Muñoz, 2014. "Is there any relationship between the rates of interest and profit in the U.S. economy?," Studies in Economics 1416, School of Economics, University of Kent.
    37. Harvey, David I. & Leybourne, Stephen J., 2016. "Improving the length of confidence sets for the date of a break in level and trend when the order of integration is unknown," Economics Letters, Elsevier, vol. 145(C), pages 239-245.
    38. Helmut Herwartz & Florian Siedenburg, 2010. "A New Approach to Unit Root Testing," Computational Economics, Springer;Society for Computational Economics, vol. 36(4), pages 365-384, December.
    39. Baumöhl, Eduard & Lyócsa, Štefan, 2012. "Constructing weekly returns based on daily stock market data: A puzzle for empirical research?," MPRA Paper 43431, University Library of Munich, Germany.
    40. Josep Lluís Carrion-i-Silvestre & María Dolores Gadea, 2013. "“GLS based unit root tests for bounded processes”," AQR Working Papers 201302, University of Barcelona, Regional Quantitative Analysis Group, revised Apr 2013.
    41. Ricardo Quineche & Gabriel Rodríguez, 2017. "Selecting the Lag Length for the M GLS Unit Root Tests with Structural Change: A Warning Note for Practitioners Based on Simulations," Econometrics, MDPI, vol. 5(2), pages 1-10, April.
    42. Anton Skrobotov, 2013. "Local Structural Trend Break in Stationarity Testing," Working Papers 0074, Gaidar Institute for Economic Policy, revised 2013.
    43. Anton Skrobotov, 2016. "On Trend Breaks and Initial Condition in Unit Root Testing," Working Papers 0097, Gaidar Institute for Economic Policy, revised 2016.
    44. Burak Alparslan Eroğlu & Barış Soybilgen, 2018. "On the Performance of Wavelet Based Unit Root Tests," JRFM, MDPI, vol. 11(3), pages 1-22, August.
    45. Natalia Bailey & Liudas Giraitis, 2015. "Spectral Approach to Parameter-Free Unit Root Testing," Working Papers 746, Queen Mary University of London, School of Economics and Finance.
    46. Josep Lluís Carrion-i-Silvestre & María Dolores Gadea & Antonio Montañés, 2017. "“Unbiased estimation of autoregressive models for bounded stochastic processes”," IREA Working Papers 201719, University of Barcelona, Research Institute of Applied Economics, revised Nov 2017.
    47. Murasawa, Yasutomo, 2015. "The multivariate Beveridge--Nelson decomposition with I(1) and I(2) series," MPRA Paper 66319, University Library of Munich, Germany.
    48. David I. Harvey & Stephen J. Leybourne & Lisa Xiao, 2009. "Testing for nonlinear trends when the order of integration is unknown," Discussion Papers 09/04, University of Nottingham, Granger Centre for Time Series Econometrics.
    49. Pujula, Aude Liliana & Zapata, Hector O., 2013. "Macroeconomic Aspects of Ghana's Export Performance," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 143039, Southern Agricultural Economics Association.
    50. Cavaliere, G. & Phillips, P.C.B. & Smeekes, S. & Taylor, A.M.R., 2011. "Lag length selection for unit root tests in the presence of nonstationary volatility," Research Memorandum 056, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    51. Herwartz, Helmut & Siedenburg, Florian, 2009. "A new approach to unit root testing," Economics Working Papers 2009-06, Christian-Albrechts-University of Kiel, Department of Economics.
    52. David I. Harvey & Stephen J. Leybourne & Lisa Xiao, 2010. "Testing for nonlinear deterministic components when the order of integration is unknown," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 379-391, September.
    53. Karsten Reichold, 2024. "A residual‐based nonparametric variance ratio no‐cointegration test," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(5), pages 847-856, September.
    54. Murasawa, Yasutomo, 2019. "Bayesian multivariate Beveridge--Nelson decomposition of I(1) and I(2) series with cointegration," MPRA Paper 91979, University Library of Munich, Germany.
    55. Zhang Jing & de Jong Robert & Haurin Donald, 2016. "Are US real house prices stationary? New evidence from univariate and panel data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 1-18, February.
    56. Topcu, Mert, 2024. "Financial market development and carbon emissions: The transmission mechanisms and the role of political corruption," Finance Research Letters, Elsevier, vol. 59(C).
    57. Costas Lapavitsas & Ivan Mendieta-MuÃ’oz, 2017. "Explaining the Historic Rise in Financial Profits in the US Economy," Working Papers 205, Department of Economics, SOAS University of London, UK.
    58. Raymond Li & David C. Broadstock, 2021. "Coal Pricing in China: Is It a Bit Too Crude?," Energies, MDPI, vol. 14(13), pages 1-13, June.
    59. David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2008. "Testing for unit roots in the presence of uncertainty over both the trend and initial condition," Discussion Papers 08/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    60. Jorge Belaire-Franch, 2019. "A note on the evidence of inflation persistence around the world," Empirical Economics, Springer, vol. 56(5), pages 1477-1487, May.
    61. Nunzio Cappuccio & Diego Lubian, 2016. "Unit Root Tests: The Role of the Univariate Models Implied by Multivariate Time Series," Econometrics, MDPI, vol. 4(2), pages 1-11, April.
    62. Walter Distaso & Rustam Ibragimov & Alexander Semenov & Anton Skrobotov, 2020. "COVID-19: Tail Risk and Predictive Regressions," Papers 2009.02486, arXiv.org, revised Oct 2021.
    63. Morten Ø. Nielsen, 2008. "A Powerful Test Of The Autoregressive Unit Root Hypothesis Based On A Tuning Parameter Free Statistic," Working Paper 1185, Economics Department, Queen's University.
    64. Almeida, Fernanda Dantas & Divino, José Angelo, 2015. "Determinants of the banking spread in the Brazilian economy: The role of micro and macroeconomic factors," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 29-39.
    65. A. Monta & L. Olmos, 2014. "Do the Spanish regions converge? A unit root analysis for the HDI of the Spanish regions," Applied Economics, Taylor & Francis Journals, vol. 46(34), pages 4218-4230, December.
    66. Bruce E. Hansen & Jeffrey S. Racine, 2018. "Bootstrap Model Averaging Unit Root Inference," Department of Economics Working Papers 2018-09, McMaster University.
    67. David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2008. "Testing for unit roots and the impact of quadratic trends, with an application to relative primary commodity prices," Discussion Papers 08/04, University of Nottingham, Granger Centre for Time Series Econometrics.
    68. Manveer Kaur Mangat & Erhard Reschenhofer, 2020. "Frequency-Domain Evidence for Climate Change," Econometrics, MDPI, vol. 8(3), pages 1-15, July.
    69. Sun, Xiaolei & Wang, Jun & Yao, Yanzhen & Li, Jingyu & Li, Jianping, 2020. "Spillovers among sovereign CDS, stock and commodity markets: A correlation network perspective," International Review of Financial Analysis, Elsevier, vol. 68(C).
    70. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Stephan Smeekes, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 398-415, May.
    71. Huang, Chao-Hsi, 2010. "International capital mobility: An alternative test based on intertemporal current account models," International Review of Economics & Finance, Elsevier, vol. 19(3), pages 467-482, June.
    72. Richard Crump & Gopi Shah Goda & Kevin Mumford, 2010. "Fertility and the Personal Exemption: Comment," NBER Working Papers 15984, National Bureau of Economic Research, Inc.
    73. Didier Nibbering & Coos van Buuren & Wei Wei, 2021. "Real Options Valuation of Wind Energy Based on the Empirical Production Uncertainty," Monash Econometrics and Business Statistics Working Papers 19/21, Monash University, Department of Econometrics and Business Statistics.
    74. Joseba Luzarraga-Goitia & Marta Regúlez-Castillo & Arturo Rodríguez-Castellanos, 2021. "The dynamics between the stock market and exchange rates: Spain 1999–2015," The European Journal of Finance, Taylor & Francis Journals, vol. 27(7), pages 655-678, May.
    75. Tomás del Barrio Castro & Denise R. Osborn & A.M. Robert Taylor, 2012. "The Performance of Lag Selection and Detrending Methods for HEGY Seasonal Unit Root Tests," Economics Discussion Paper Series 1228, Economics, The University of Manchester.
    76. David I. Harvey, & Stephen J. Leybourne, & A. M. Robert Taylor, 2007. "Testing for a unit root when uncertain about the trend [Revised to become 07/03 above]," Discussion Papers 06/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    77. Pierre Perron & Francisco Estrada & Carlos Gay-García & Benjamín Martínez-López, 2011. "A time-series analysis of the 20th century climate simulations produced for the IPCC’s AR4," Boston University - Department of Economics - Working Papers Series WP2011-051, Boston University - Department of Economics.
    78. Diego Romero-Ávila, 2013. "Is Physical Investment The Key To China'S Growth Miracle?," Economic Inquiry, Western Economic Association International, vol. 51(4), pages 1948-1971, October.
    79. Wojciech Charemza & Carlos Díaz & Svetlana Makarova, 2015. "Ex-post Inflation Forecast Uncertainty and Skew Normal Distribution: ‘Back from the Future’ Approach," Discussion Papers in Economics 15/09, Division of Economics, School of Business, University of Leicester.
    80. David Harvey & Stephen Leybourne, 2014. "Confidence sets for the date of a break in level and trend when the order of integration is unknown," Discussion Papers 14/04, University of Nottingham, Granger Centre for Time Series Econometrics.
    81. Vicente Esteve & Cecilio Tamarit, 2018. "Public debt and economic growth in Spain, 1851–2013," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 12(2), pages 219-249, May.
    82. Antonio E. Noriega & Daniel Ventosa-Santaularia, 2011. "A Simple Test for Spurious Regressions," CREATES Research Papers 2011-15, Department of Economics and Business Economics, Aarhus University.
    83. Karsten Reichold, 2022. "A Residuals-Based Nonparametric Variance Ratio Test for Cointegration," Papers 2211.06288, arXiv.org, revised Dec 2022.
    84. Niels Haldrup & Robinson Kruse & Timo Teräsvirta & Rasmus T. Varneskov, 2012. "Unit roots, nonlinearities and structural breaks," CREATES Research Papers 2012-14, Department of Economics and Business Economics, Aarhus University.
    85. David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2007. "Unit root testing in practice: dealing with uncertainty over the trend and initial condition," Discussion Papers 07/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    86. Noriega Antonio E. & Ramos Francia Manuel, 2009. "On the dynamics of inflation persistence around the world," Working Papers 2009-02, Banco de México.
    87. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    88. Matteo Mogliani & Giovanni Urga & Carlos Winograd, 2009. "Monetary disorder and financial regimes - The demand for money in Argentina, 1900-2006," PSE Working Papers halshs-00575107, HAL.
    89. Ivan Mendieta-Muñoz, 2017. "Explaining the Historic Rise in Financial Profits in the U.S. Economy JEL Classification: E11, E44, G20," Working Paper Series, Department of Economics, University of Utah 2017_06, University of Utah, Department of Economics.
    90. David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2010. "Unit root testing under a local break in trend," Discussion Papers 10/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    91. Francisco Estrada & Pierre Perron, "undated". "Detection and attribution of climate change through econometric methods," Boston University - Department of Economics - Working Papers Series 2013-015, Boston University - Department of Economics.
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    93. David I. Harvey & Stephen J. Leybourne & A.M. Robert Taylor, 2014. "Unit Root Testing under a Local Break in Trend using Partial Information on the Break Date," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 93-111, February.
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    100. Roman Horváth & Štefan Lyócsa & Eduard Baumöhl, 2018. "Stock market contagion in Central and Eastern Europe: unexpected volatility and extreme co-exceedance," The European Journal of Finance, Taylor & Francis Journals, vol. 24(5), pages 391-412, March.
    101. Charemza, Wojciech & Díaz, Carlos & Makarova, Svetlana, 2019. "Quasi ex-ante inflation forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 35(3), pages 994-1007.
    102. Mollick, André Varella, 2009. "Employment Responses of Skilled and Unskilled Workers at Mexican Maquiladoras: The Effects of External Factors," World Development, Elsevier, vol. 37(7), pages 1285-1296, July.
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    248. MB Hendrie Anto & Faaza Fakhrunnas & Yunice Karina Tumewang, 2022. "Islamic banks credit risk performance for home financing: Before and during Covid-19 pandemic," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 14(1), pages 113-125.
    249. Bertille Antoine & Otilia Boldea, 2014. "Efficient Inference with Time-Varying Identification Strength," Discussion Papers dp14-03, Department of Economics, Simon Fraser University.
    250. Švabovič Miroslav & Miškinis Algirdas, 2016. "A Quantitative Analysis of the Main Lithuanian Taxes and Their Optimisation During the Crisis," Ekonomika (Economics), Sciendo, vol. 95(3), pages 98-111, December.
    251. Mahua Barari & Srikanta Kundu, 2019. "The Role of the Federal Reserve in the U.S. Housing Crisis: A VAR Analysis with Endogenous Structural Breaks," JRFM, MDPI, vol. 12(3), pages 1-20, July.

Articles

  1. Zhongjun Qu & Jungmo Yoon & Pierre Perron, 2024. "Inference on Conditional Quantile Processes in Partially Linear Models with Applications to the Impact of Unemployment Benefits," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 521-541, March.

    Cited by:

    1. Song, Xiaojun & Yang, Zixin, 2025. "Unified specification tests in partially linear quantile regression models," Statistics & Probability Letters, Elsevier, vol. 216(C).
    2. Felder, Rahel & Frings, Hanna & Mittag, Nikolas, 2024. "How does potential unemployment insurance benefit duration affect reemployment timing and wages?," Ruhr Economic Papers 1111, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  2. Zhongjun Qu & Fan Zhuo, 2021. "Likelihood Ratio-Based Tests for Markov Regime Switching," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(2), pages 937-968.
    See citations under working paper version above.
  3. Lu, Junwen & Qu, Zhongjun, 2021. "Sieve estimation of option-implied state price density," Journal of Econometrics, Elsevier, vol. 224(1), pages 88-112.

    Cited by:

    1. Li, Yifan & Nolte, Ingmar & Pham, Manh Cuong, 2024. "Parametric risk-neutral density estimation via finite lognormal-Weibull mixtures," Journal of Econometrics, Elsevier, vol. 241(2).

  4. Zhongjun Qu & Jungmo Yoon, 2019. "Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 625-647, October.
    See citations under working paper version above.
  5. Zhongjun Qu, 2018. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 916-932, December.
    See citations under working paper version above.
  6. Zhongjun Qu & Denis Tkachenko, 2017. "Global Identification in DSGE Models Allowing for Indeterminacy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(3), pages 1306-1345.
    See citations under working paper version above.
  7. Yi-Ting Chen & Zhongjun Qu, 2015. "M Tests with a New Normalization Matrix," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 617-652, May.
    See citations under working paper version above.
  8. Qu, Zhongjun & Yoon, Jungmo, 2015. "Nonparametric estimation and inference on conditional quantile processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 1-19.
    See citations under working paper version above.
  9. Zhongjun Qu, 2014. "Inference in dynamic stochastic general equilibrium models with possible weak identification," Quantitative Economics, Econometric Society, vol. 5, pages 457-494, July.

    Cited by:

    1. Kilian, Lutz & Inoue, Atsushi & Guerron-Quintana, Pablo A., 2014. "Impulse Response Matching Estimators for DSGE Models," CEPR Discussion Papers 10298, C.E.P.R. Discussion Papers.
    2. Willi Mutschler, 2014. "Identification of DSGE Models - the Effect of Higher-Order Approximation and Pruning," CQE Working Papers 3314, Center for Quantitative Economics (CQE), University of Muenster.
    3. Zhongjun Qu, 2018. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 916-932, December.
    4. Linde, Jesper & LASEEN, PER & Ratto, Marco, 2019. "Identification Versus Misspecification in New Keynesian Monetary Policy Models," CEPR Discussion Papers 13492, C.E.P.R. Discussion Papers.
    5. Tan, Fei, 2018. "A Frequency-Domain Approach to Dynamic Macroeconomic Models," MPRA Paper 90487, University Library of Munich, Germany.
    6. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    7. Komunjer, Ivana & Zhu, Yinchu, 2020. "Likelihood ratio testing in linear state space models: An application to dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 218(2), pages 561-586.
    8. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    9. Zhongjun Qu & Denis Tkachenko, 2023. "Using arbitrary precision arithmetic to sharpen identification analysis for DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 644-667, June.
    10. Prosper Dovonon & Alastair R. Hall, 2017. "The Asymptotic Properties of GMM and Indirect Inference Under Second-Order Identification," Economics Discussion Paper Series 1705, Economics, The University of Manchester.
    11. Dovonon, Prosper & Hall, Alastair R., 2018. "The asymptotic properties of GMM and indirect inference under second-order identification," Journal of Econometrics, Elsevier, vol. 205(1), pages 76-111.
    12. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    13. Cheng, Xu, 2015. "Robust inference in nonlinear models with mixed identification strength," Journal of Econometrics, Elsevier, vol. 189(1), pages 207-228.

  10. Zhongjun Qu & Pierre Perron, 2013. "A stochastic volatility model with random level shifts and its applications to S&P 500 and NASDAQ return indices," Econometrics Journal, Royal Economic Society, vol. 16(3), pages 309-339, October.

    Cited by:

    1. Márcio Poletti Laurini & Roberto Baltieri Mauad & Fernando Antonio Lucena Aiube, 2016. "Multivariate Stochastic Volatility-Double Jump Model: an application for oil assets," Working Papers Series 415, Central Bank of Brazil, Research Department.
    2. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    3. Jiawen Xu & Pierre Perron, 2023. "Forecasting in the presence of in-sample and out-of-sample breaks," Empirical Economics, Springer, vol. 64(6), pages 3001-3035, June.
    4. Chen, Yan & Zhang, Lei & Bouri, Elie, 2024. "Can a self-exciting jump structure better capture the jump behavior of cryptocurrencies? A comparative analysis with the S&P 500," Research in International Business and Finance, Elsevier, vol. 69(C).
    5. Laurini, Márcio Poletti & Mauad, Roberto Baltieri & Aiube, Fernando Antônio Lucena, 2020. "The impact of co-jumps in the oil sector," Research in International Business and Finance, Elsevier, vol. 52(C).
    6. Laurini, Márcio Poletti & Mauad, Roberto Baltieri, 2015. "A common jump factor stochastic volatility model," Finance Research Letters, Elsevier, vol. 12(C), pages 2-10.
    7. Patrick Krieger & Carsten Lausberg & Kristin Wellner, 2018. "Einblicke in die Gründe für nicht-normalverteilte Immobilienrenditen: eine explorative Untersuchung deutscher Wohnimmobilienportfolios [Insights into the reasons for non-normal real estate returns:," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 4(1), pages 49-79, November.
    8. Marie Busch & Philipp Sibbertsen, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Econometrics, MDPI, vol. 6(1), pages 1-21, March.
    9. 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.
    10. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    11. Jiawen Xu & Pierre Perron, 2015. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series wp2015-012, Boston University - Department of Economics.
    12. 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.
    13. Márcio P. Laurini & Roberto B. Mauad, 2014. "The stochastic volatility model with random jumps and its application to BRL/USD exchange rate," Economics Bulletin, AccessEcon, vol. 34(2), pages 1002-1011.
    14. 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.
    15. Kruse, Robinson, 2015. "A modified test against spurious long memory," Economics Letters, Elsevier, vol. 135(C), pages 34-38.
    16. 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.
    17. Niels Haldrup & Robinson Kruse, 2014. "Discriminating between fractional integration and spurious long memory," CREATES Research Papers 2014-19, Department of Economics and Business Economics, Aarhus University.
    18. Cássio R. A. Alves & Márcio P. Laurini, 2022. "Measuring inflation persistence under time-varying inflation target and stochastic volatility with jumps," Economics Bulletin, AccessEcon, vol. 42(2), pages 342-349.
    19. Dennis Alvaro & Ángel Guillén & Gabriel Rodríguez, 2017. "Modelling the volatility of commodities prices using a stochastic volatility model with random level shifts," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 153(1), pages 71-103, February.
    20. Fernando Antonio Lucena Aiube & Ariel Levy, 2019. "Recent movement of oil prices and future scenarios [Movimentos recentes dos preços do petróleo e os cenários futuros]," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 29(1), pages 223-248, January-A.
    21. Rodolfo C. Moura & Márcio P. Laurini, 2021. "Spillovers and jumps in global markets: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5997-6013, October.
    22. 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.
    23. Chaim, Pedro & Laurini, Márcio P., 2019. "Nonlinear dependence in cryptocurrency markets," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 32-47.
    24. Chaim, Pedro & Laurini, Márcio P., 2018. "Volatility and return jumps in bitcoin," Economics Letters, Elsevier, vol. 173(C), pages 158-163.
    25. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2015. "Shifts in volatility driven by large stock market shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 130-147.
    26. Seong Yeon Chang & Pierre Perron, 2017. "Fractional Unit Root Tests Allowing for a Structural Change in Trend under Both the Null and Alternative Hypotheses," Econometrics, MDPI, vol. 5(1), pages 1-26, January.
    27. Luo, Deqing & Pang, Tao & Xu, Jiawen, 2021. "Forecasting U.S. Yield Curve Using the Dynamic Nelson–Siegel Model with Random Level Shift Parameters," Economic Modelling, Elsevier, vol. 94(C), pages 340-350.
    28. Alanya-Beltran, Willy, 2022. "Modelling stock returns volatility with dynamic conditional score models and random shifts," Finance Research Letters, Elsevier, vol. 45(C).

  11. Zhongjun Qu & Denis Tkachenko, 2012. "Identification and frequency domain quasi‐maximum likelihood estimation of linearized dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 3(1), pages 95-132, March.

    Cited by:

    1. Khalaf, Lynda & Lin, Zhenjiang, 2021. "Projection-based inference with particle swarm optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    2. Andrzej Kocięcki & Marcin Kolasa, 2022. "A solution to the global identification problem in DSGE models," Working Papers 2022-01, Faculty of Economic Sciences, University of Warsaw.
    3. Benchimol, Jonathan & Bounader, Lahcen & Dotta, Mario, 2025. "Estimating Behavioral Inattention," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 236, pages 1-34.
    4. Mutschler, Willi, 2014. "Identification of DSGE Models - A Comparison of Methods and the Effect of Second Order Approximation," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100598, Verein für Socialpolitik / German Economic Association.
    5. Marianna Riggi & Sergio Santoro, 2015. "On the Slope and the Persistence of the Italian Phillips Curve," International Journal of Central Banking, International Journal of Central Banking, vol. 11(2), pages 157-197, March.
    6. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Willi Mutschler, 2015. "Higher-order statistics for DSGE models," CQE Working Papers 4315, Center for Quantitative Economics (CQE), University of Muenster.
    8. Stéphane Adjemian & Houtan Bastani & Michel Juillard & Frédéric Karamé & Ferhat Mihoubi & Willi Mutschler & Johannes Pfeifer & Marco Ratto & Sébastien Villemot & Normann Rion, 2023. "Dynare: Reference Manual Version 5," PSE Working Papers hal-04219920, HAL.
      • Stéphane Adjemian & Houtan Bastani & Michel Juillard & Frédéric Karamé & Ferhat Mihoubi & Willi Mutschler & Johannes Pfeifer & Marco Ratto & Sébastien Villemot & Normann Rion, 2023. "Dynare: Reference Manual Version 5," Working Papers hal-04219920, HAL.
      • Adjemian, Stéphane & Bastani, Houtan & Juillard, Michel & Karamé, Fréderic & Mihoubi, Ferhat & Mutschler, Willi & Pfeifer, Johannes & Ratto, Marco & Rion, Normann & Villemot, Sébastien, 2022. "Dynare: Reference Manual Version 5," Dynare Working Papers 72, CEPREMAP, revised Mar 2023.
    9. Jonathan Benchimol & Lahcen Bounader, 2018. "Optimal Monetary Policy Under Bounded Rationality," Globalization Institute Working Papers 336, Federal Reserve Bank of Dallas.
    10. Hsiao, Cody Yu-Ling & Jin, Tao & Kwok, Simon & Wang, Xi & Zheng, Xin, 2023. "Entrepreneurial risk shocks and financial acceleration asymmetry in a two-country DSGE model," China Economic Review, Elsevier, vol. 81(C).
    11. Willi Mutschler, 2014. "Identification of DSGE Models - the Effect of Higher-Order Approximation and Pruning," CQE Working Papers 3314, Center for Quantitative Economics (CQE), University of Muenster.
    12. Pedro Brinca & Nikolay Iskrev & Francesca Loria, 2022. "On Identification Issues in Business Cycle Accounting Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 55-138, Emerald Group Publishing Limited.
    13. Zhongjun Qu & Fan Zhuo, 2015. "Likelihood Ratio Based Tests for Markov Regime Switching," Boston University - Department of Economics - Working Papers Series wp2015-003, Boston University - Department of Economics.
    14. Yasuo Hirose & Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2024. "Estimating a Behavioral New Keynesian Model with the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(8), pages 2185-2197, December.
    15. Adjemian, Stéphane & Juillard, Michel & Karamé, Fréderic & Mutschler, Willi & Pfeifer, Johannes & Ratto, Marco & Rion, Normann & Villemot, Sébastien, 2024. "Dynare: Reference Manual, Version 6," Dynare Working Papers 80, CEPREMAP, revised Jun 2025.
    16. Bai, Jushan & Wang, Peng, 2014. "Identification theory for high dimensional static and dynamic factor models," Journal of Econometrics, Elsevier, vol. 178(2), pages 794-804.
    17. Andrzej Kociecki & Marcin Kolasa, 2013. "Global identification of linearized DSGE models," NBP Working Papers 170, Narodowy Bank Polski.
    18. Donald W. K. Andrews & Patrik Guggenberger, 2015. "Identification- and Singularity-Robust Inference for Moment Condition," Cowles Foundation Discussion Papers 1978, Cowles Foundation for Research in Economics, Yale University.
    19. Morris, Stephen D., 2020. "Is the Taylor principle still valid when rates are low?," Journal of Macroeconomics, Elsevier, vol. 64(C).
    20. Peter A. Zadrozny, 2022. "Linear Identification of Linear Rational-Expectations Models by Exogenous Variables Reconciles Lucas and Sims," CESifo Working Paper Series 10078, CESifo.
    21. Enrique Martínez García, 2020. "A Matter of Perspective: Mapping Linear Rational Expectations Models into Finite-Order VAR Form," Globalization Institute Working Papers 389, Federal Reserve Bank of Dallas.
    22. Zhongjun Qu, 2018. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 916-932, December.
    23. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2017. "A Monte Carlo procedure for checking identification in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 202-210.
    24. Morris, Stephen D., 2016. "VARMA representation of DSGE models," Economics Letters, Elsevier, vol. 138(C), pages 30-33.
    25. Sergey Ivashchenko & Willi Mutschler, 2019. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," CQE Working Papers 8319, Center for Quantitative Economics (CQE), University of Muenster.
    26. Josué Diwambuena & Raquel Fonseca & Stefan Schubert, 2021. "Italian Labour Frictions and Wage Rigidities in an Estimated DSGE," Cahiers de recherche / Working Papers 2105, Chaire de recherche sur les enjeux économiques intergénérationnels / Research Chair in Intergenerational Economics.
    27. Emanuele Bacchiocchi & Toru Kitagawa, 2025. "Locally- but not Globally-identified SVARs," Papers 2504.01441, arXiv.org.
    28. Tan, Fei, 2018. "A Frequency-Domain Approach to Dynamic Macroeconomic Models," MPRA Paper 90487, University Library of Munich, Germany.
    29. Stephen Morris, 2014. "The Statistical Implications of Common Identifying Restrictions for DSGE Models," 2014 Meeting Papers 738, Society for Economic Dynamics.
    30. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    31. Komunjer, Ivana & Zhu, Yinchu, 2020. "Likelihood ratio testing in linear state space models: An application to dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 218(2), pages 561-586.
    32. Prosper Dovonon & Alastair Hall, 2018. "The Asymptotic Properties of GMM and Indirect Inference under Second-order Identification," CIRANO Working Papers 2018s-37, CIRANO.
    33. Enrique Martínez García & Mark A. Wynne, 2014. "Assessing Bayesian model comparison in small samples," Globalization Institute Working Papers 189, Federal Reserve Bank of Dallas.
    34. Zhongjun Qu & Denis Tkachenko, 2023. "Using arbitrary precision arithmetic to sharpen identification analysis for DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 644-667, June.
    35. García, Carlos J., 2025. "Economic impact and policies for the obesity pandemic in emerging economies," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1949-1970.
    36. Prosper Dovonon & Alastair R. Hall, 2017. "The Asymptotic Properties of GMM and Indirect Inference Under Second-Order Identification," Economics Discussion Paper Series 1705, Economics, The University of Manchester.
    37. Dovonon, Prosper & Hall, Alastair R., 2018. "The asymptotic properties of GMM and indirect inference under second-order identification," Journal of Econometrics, Elsevier, vol. 205(1), pages 76-111.
    38. Anna Mikusheva, 2014. "Estimation of dynamic stochastic general equilibrium models (in Russian)," Quantile, Quantile, issue 12, pages 1-21, February.
    39. Juan Carlos Parra‐Alvarez & Olaf Posch & Mu‐Chun Wang, 2023. "Estimation of Heterogeneous Agent Models: A Likelihood Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 304-330, April.
    40. Iskrev, Nikolay, 2019. "What to expect when you're calibrating: Measuring the effect of calibration on the estimation of macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 99(C), pages 54-81.
    41. Tan, Fei, 2017. "An analytical approach to new Keynesian models under the fiscal theory," Economics Letters, Elsevier, vol. 156(C), pages 133-137.
    42. Giovanni Nicolò, 2025. "US Monetary Policy and Indeterminacy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(2), pages 195-213, March.
    43. Christensen, Bent Jesper & Neri, Luca & Parra-Alvarez, Juan Carlos, 2024. "Estimation of continuous-time linear DSGE models from discrete-time measurements," Journal of Econometrics, Elsevier, vol. 244(2).
    44. Szabolcs Deak & Paul Levine & Afrasiab Mirza & Son Pham, 2023. "Negotiating the Wilderness of Bounded Rationality through Robust Policy," School of Economics Discussion Papers 0223, School of Economics, University of Surrey.
    45. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "Testing DSGE Models by indirect inference: a survey of recent findings," Cardiff Economics Working Papers E2018/14, Cardiff University, Cardiff Business School, Economics Section.
    46. Pedro Chaim & Márcio Poletti Laurini, 2022. "Data Cloning Estimation and Identification of a Medium-Scale DSGE Model," Stats, MDPI, vol. 6(1), pages 1-13, December.
    47. Tan, Fei & Walker, Todd B., 2015. "Solving generalized multivariate linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 95-111.
    48. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    49. Meyer-Gohde, Alexander & Tzaawa-Krenzler, Mary, 2023. "Sticky information and the Taylor principle," IMFS Working Paper Series 189, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    50. Morris, Stephen D., 2017. "DSGE pileups," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 56-86.
    51. Marco Lorusso & Francesco Ravazzolo & Claudia Udroiu, 2024. "Fiscal stimuli: Monetary versus Fiscal Financing," BEMPS - Bozen Economics & Management Paper Series BEMPS105, Faculty of Economics and Management at the Free University of Bozen.
    52. Giovanni Angelini & Luca Fanelli & Marco M. Sorge, 2025. "Is Time an Illusion? A Bootstrap Likelihood Ratio Test for Shock Transmission Delays in DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2477-2503, May.
    53. Majid M. Al-Sadoon, 2020. "The Spectral Approach to Linear Rational Expectations Models," Papers 2007.13804, arXiv.org, revised Aug 2024.
    54. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers 21/13, Institute for Fiscal Studies.
    55. Nikolay Iskrev, 2013. "On the distribution of information in the moment structure of DSGE models," 2013 Meeting Papers 339, Society for Economic Dynamics.

  12. Qu, Zhongjun, 2011. "A Test Against Spurious Long Memory," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 423-438.
    See citations under working paper version above.
  13. Oka, Tatsushi & Qu, Zhongjun, 2011. "Estimating structural changes in regression quantiles," Journal of Econometrics, Elsevier, vol. 162(2), pages 248-267, June.
    See citations under working paper version above.
  14. Perron, Pierre & Qu, Zhongjun, 2010. "Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 275-290.
    See citations under working paper version above.
  15. Qu, Zhongjun, 2008. "Testing for structural change in regression quantiles," Journal of Econometrics, Elsevier, vol. 146(1), pages 170-184, September.

    Cited by:

    1. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2020. "Can systemic risk measures predict economic shocks? Evidence from China," China Economic Review, Elsevier, vol. 64(C).
    2. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    3. Tillmann, Peter & Wolters, Maik Hendrik, 2012. "The changing dynamics of US inflation persistence: A quantile regression approach," IMFS Working Paper Series 60, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    4. Gabriela Ciuperca & Zahraa Salloum, 2015. "Empirical likelihood test in a posteriori change-point nonlinear model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 919-952, November.
    5. Bonaccolto, G. & Caporin, M. & Gupta, R., 2018. "The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 446-469.
    6. Christian Bauer & Sebastian Weber, 2016. "The Efficiency of Monetary Policy when Guiding Inflation Expectations," Research Papers in Economics 2016-14, University of Trier, Department of Economics.
    7. Marilena Furno, 2011. "Goodness of Fit and Misspecification in Quantile Regressions," Journal of Educational and Behavioral Statistics, , vol. 36(1), pages 105-131, February.
    8. Rangan Gupta & Charl Jooste & Omid Ranjbar, 2017. "South Africa’s inflation persistence: a quantile regression framework," Economic Change and Restructuring, Springer, vol. 50(4), pages 367-386, November.
    9. Ghysels, Eric & Liu, Hanwei, 2017. "Downside Risk in the Chinese Stock Market - Has it Fundamentally Changed?," CEPR Discussion Papers 12180, C.E.P.R. Discussion Papers.
    10. Yi-Ting Chen & Zhongjun Qu, 2015. "M Tests with a New Normalization Matrix," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 617-652, May.
    11. Tatsushi Oka & Pierre Perron, 2018. "Testing for common breaks in a multiple equations system," Monash Econometrics and Business Statistics Working Papers 3/18, Monash University, Department of Econometrics and Business Statistics.
    12. Yannick Hoga & Christian Schulz, 2025. "Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series," Papers 2502.10065, arXiv.org, revised Jun 2025.
    13. Alexander Aue & Rex C. Y. Cheung & Thomas C. M. Lee & Ming Zhong, 2014. "Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1241-1256, September.
    14. Zhongjun Qu & Tatsushi Oka, 2010. "Estimating structural changes in regression quantiles," Boston University - Department of Economics - Working Papers Series WP2010-052, Boston University - Department of Economics.
    15. Hosseinkouchack, Mehdi & Wolters, Maik H., 2013. "Do large recessions reduce output permanently?," Economics Letters, Elsevier, vol. 121(3), pages 516-519.
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    19. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    20. Yannick Hoga, 2024. "Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions," Papers 2410.05861, arXiv.org.
    21. Massimiliano Caporin & Rangan Gupta & Francesco Ravazzolo, 2019. "Contagion between Real Estate and Financial Markets: A Bayesian Quantile-on-Quantile Approach," BEMPS - Bozen Economics & Management Paper Series BEMPS61, Faculty of Economics and Management at the Free University of Bozen.
    22. Ji, Qiang & Zhang, Dayong & Zhao, Yuqian, 2020. "Searching for safe-haven assets during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 71(C).
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    26. Fenghua Wen & Kaiyan Weng & Wei-Xing Zhou, 2020. "Measuring the contribution of Chinese financial institutions to systemic risk: an extended asymmetric CoVaR approach," Risk Management, Palgrave Macmillan, vol. 22(4), pages 310-337, December.
    27. Zhou, Mi & Wang, Huixia Judy & Tang, Yanlin, 2015. "Sequential change point detection in linear quantile regression models," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 98-103.
    28. Uribe, Jorge M. & Chuliá, Helena & Guillén, Montserrat, 2017. "Uncertainty, systemic shocks and the global banking sector: Has the crisis modified their relationship?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 52-68.
    29. Emanuele Russo & Neil Foster-McGregor, 2022. "Characterizing growth instability: new evidence on unit roots and structural breaks in countries’ long run trajectories," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 713-756, April.
    30. Xiaochun Liu, 2018. "How is the Taylor Rule Distributed under Endogenous Monetary Regimes?," International Review of Finance, International Review of Finance Ltd., vol. 18(2), pages 305-316, June.
    31. Galvao Jr., Antonio F., 2009. "Unit root quantile autoregression testing using covariates," Journal of Econometrics, Elsevier, vol. 152(2), pages 165-178, October.
    32. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
    33. Chung-Ming Kuan & Christos Michalopoulos & Zhijie Xiao, 2017. "Quantile Regression on Quantile Ranges – A Threshold Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 99-119, January.
    34. Tolga Omay & Rangan Gupta & Giovanni Bonaccolto, 2015. "The US Real GNP is Trend-Stationary After All," Working Papers 201581, University of Pretoria, Department of Economics.
    35. Feipeng Zhang & Qunhua Li, 2023. "Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high‐throughput experiments," Biometrics, The International Biometric Society, vol. 79(3), pages 2272-2285, September.
    36. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    37. Liu, Xiaochun, 2017. "Measuring systemic risk with regime switching in tails," Economic Modelling, Elsevier, vol. 67(C), pages 55-72.
    38. Alia Afzal & Philipp Sibbertsen, 2023. "Long Memory, Spurious Memory: Persistence in Range-Based Volatility of Exchange Rates," Open Economies Review, Springer, vol. 34(4), pages 789-811, September.
    39. Wolters, Maik Hendrik, 2010. "Estimating Monetary Policy Reaction Functions Using Quantile Regressions," MPRA Paper 23857, University Library of Munich, Germany.
    40. Daiqing Xi & Tianxiao Pang, 2021. "Estimating multiple breaks in mean sequentially with fractionally integrated errors," Statistical Papers, Springer, vol. 62(1), pages 451-494, February.
    41. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 244(2).
    42. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    43. Lin Fan & Peter W. Glynn & Markus Pelger, 2018. "Change-Point Testing for Risk Measures in Time Series," Papers 1809.02303, arXiv.org, revised Jul 2023.
    44. Marilena Furno, 2021. "Cointegration tests at the quantiles," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1087-1100, January.
    45. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2021. "Systemic risk measures and distribution forecasting of macroeconomic shocks," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 178-196.
    46. Liu, Weiqiang, 2023. "A consistent nonparametric test for the structure change in quantile regression," Economics Letters, Elsevier, vol. 228(C).
    47. Marilena Furno, 2012. "Tests for structural break in quantile regressions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(4), pages 493-515, October.
    48. Sangyeol Lee & Chang Kyeom Kim, 2024. "Test for conditional quantile change in general conditional heteroscedastic time series models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(2), pages 333-359, April.
    49. Chong, Terence Tai Leung & Pang, Tianxiao & Zhang, Danna & Liang, Yanling, 2017. "Structural change in non-stationary AR(1) models," MPRA Paper 80510, University Library of Munich, Germany.
    50. Zhang, Feipeng & Li, Qunhua, 2017. "A continuous threshold expectile model," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 49-66.
    51. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
    52. Kuriyama Nina, 2016. "Testing cointegration in quantile regressions with an application to the term structure of interest rates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 107-121, April.
    53. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
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    58. Chiou, Yan-Yu & Chen, Mei-Yuan & Chen, Jau-er, 2018. "Nonparametric regression with multiple thresholds: Estimation and inference," Journal of Econometrics, Elsevier, vol. 206(2), pages 472-514.
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  16. Qu, Zhongjun & Perron, Pierre, 2007. "A Modified Information Criterion For Cointegration Tests Based On A Var Approximation," Econometric Theory, Cambridge University Press, vol. 23(4), pages 638-685, August.
    See citations under working paper version above.
  17. Perron, Pierre & Qu, Zhongjun, 2007. "A simple modification to improve the finite sample properties of Ng and Perron's unit root tests," Economics Letters, Elsevier, vol. 94(1), pages 12-19, January.
    See citations under working paper version above.
  18. Zhongjun Qu & Pierre Perron, 2007. "Estimating and Testing Structural Changes in Multivariate Regressions," Econometrica, Econometric Society, vol. 75(2), pages 459-502, March.
    See citations under working paper version above.
  19. Zhongjun Qu, 2007. "Searching for cointegration in a dynamic system," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 580-604, November.

    Cited by:

    1. Matteo Mogliani, 2010. "Residual-based tests for cointegration and multiple deterministic structural breaks: A Monte Carlo study," PSE Working Papers halshs-00564897, HAL.
    2. Ahmed, Walid M.A. & Sleem, Mohamed A.E., 2023. "Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach," Energy Economics, Elsevier, vol. 124(C).
    3. Karsten Schweikert, 2022. "Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 83-104, January.
    4. Clark, Steven P. & Coggin, T. Daniel, 2011. "Was there a U.S. house price bubble? An econometric analysis using national and regional panel data," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 189-200, May.
    5. Mohitosh Kejriwal & Pierre Perron, 2006. "Testing for Multiple Structural Changes in Cointegrated Regression Models," Boston University - Department of Economics - Working Papers Series WP2006-051, Boston University - Department of Economics.
    6. Peri, Massimo & Baldi, Lucia, 2013. "The effect of biofuel policies on feedstock market: Empirical evidence for rapeseed oil prices in EU," Resource and Energy Economics, Elsevier, vol. 35(1), pages 18-37.
    7. Wang, Yiming, 2011. "The stability of long-run money demand in the United States: A new approach," Economics Letters, Elsevier, vol. 111(1), pages 60-63, April.
    8. Luis F. Martins & Paulo M. M. Rodrigues, 2022. "Correction to: Tests for segmented cointegration: an application to US governments budgets," Empirical Economics, Springer, vol. 63(2), pages 601-601, August.
    9. Davidson, James & Monticini, Andrea, 2010. "Tests for cointegration with structural breaks based on subsamples," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2498-2511, November.
    10. Anton Skrobotov, 2021. "Structural breaks in cointegration models: Multivariate case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 83-106.
    11. Pierre Perron & Yohei Yamamoto, 2018. "Testing for Changes in Forecasting Performance," Boston University - Department of Economics - Working Papers Series WP2019-03, Boston University - Department of Economics, revised Dec 2018.
    12. Lukáš ČECHURA & Tereza TAUSSIGOVÁ, 2013. "Avian influenza and structural change in the Czech poultry industry," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 59(1), pages 38-47.
    13. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2021. "Time-Varying Comovement of Foreign Exchange Markets: A GLS-Based Time-Varying Model Approach," Mathematics, MDPI, vol. 9(8), pages 1-13, April.
    14. Karsten Schweikert, 2020. "Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions," Papers 2001.07949, arXiv.org, revised Apr 2021.
    15. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2016. "Time-Varying Comovement of Foreign Exchange Markets," Papers 1610.04334, arXiv.org.
    16. Razvan Pascalau & Junsoo Lee & Saban Nazlioglu & Yan (Olivia) Lu, 2022. "Johansen‐type cointegration tests with a Fourier function," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 828-852, September.
    17. Martins, Luis F. & Gabriel, Vasco J., 2014. "Modelling long run comovements in equity markets: A flexible approach," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 288-295.

  20. Perron, Pierre & Qu, Zhongjun, 2006. "Estimating restricted structural change models," Journal of Econometrics, Elsevier, vol. 134(2), pages 373-399, October.

    Cited by:

    1. Georges Dionne & Olfa Maalaoui Chun, 2013. "Default and liquidity regimes in the bond market during the 2002-2012 period," Working Papers 13-4, HEC Montreal, Canada Research Chair in Risk Management.
    2. Seong Yeon Chang & Pierre Perron, 2013. "A Comparison of Alternative Methods to Construct Confidence Intervals for the Estimate of a Break Date in Linear Regression Models," Boston University - Department of Economics - Working Papers Series wp2015-010, Boston University - Department of Economics, revised 11 Oct 2015.
    3. Georges Dionne & Olfa Maalaoui Chun, 2013. "Presidential Address: Default and liquidity regimes in the bond market during the 2002–2012 period," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 46(4), pages 1160-1195, November.
    4. Boonsoo Koo & Myung Hwan Seo, 2013. "Structural-break models under mis-specification: implications for forecasting," Monash Econometrics and Business Statistics Working Papers 11/13, Monash University, Department of Econometrics and Business Statistics.
    5. Giorgio Canarella & Rangan Gupta & Stephen M. Miller & Stephen K. Pollard, 2017. "Unemployment Rate Hysteresis and the Great Recession: Exploring the Metropolitan Evidence," Working Papers 201740, University of Pretoria, Department of Economics.
    6. Wang, Shaoping & Cui, Guowei & Li, Kunpeng, 2015. "Factor-augmented regression models with structural change," Economics Letters, Elsevier, vol. 130(C), pages 124-127.
    7. Richard Bluhm & Denis de Crombrugghe & Adam Szirmai, 0. "Do Weak Institutions Prolong Crises? On the Identification, Characteristics, and Duration of Declines during Economic Slumps," The World Bank Economic Review, World Bank, vol. 34(3), pages 810-832.
    8. Luis Garicano & Claire LeLarge & John Van Reenen, 2013. "Firm Size Distortions and the Productivity Distribution: Evidence from France," NBER Working Papers 18841, National Bureau of Economic Research, Inc.
    9. Mohitosh Kejriwal & Xuewen Yu & Pierre Perron, 2020. "Bootstrap Procedures for Detecting Multiple Persistence Shifts in Heteroskedastic Time Series," Boston University - Department of Economics - Working Papers Series WP2020-009, Boston University - Department of Economics.
    10. Pierre Perron & Yohei Yamamoto, 2011. "Estimating and Testing Multiple Structural Changes in Linear Models Using Band Spectral Regressions," Boston University - Department of Economics - Working Papers Series WP2011-049, Boston University - Department of Economics.
    11. Otilia Boldea & Alastair R. Hall, 2012. "Estimation and Inference in Unstable Nonlinear Least Squares Models," Centre for Growth and Business Cycle Research Discussion Paper Series 174, Economics, The University of Manchester.
    12. Josep Lluís Carrion-i-Silvestre & Dukpa Kim & Pierre Perron, 2007. "GLS-based unit root tests with multiple structural breaks both under the null and the alternative hypotheses," Boston University - Department of Economics - Working Papers Series wp2008-019, Boston University - Department of Economics.
    13. Akiko Sakanishi, 2020. "Urban commuting behavior and time allocation among women: Evidence from US metropolitan areas," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(2), pages 349-363, April.
    14. Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2015. "The Asymptotic Behaviour of the Residual Sum of Squares in Models with Multiple Break Points," Economics Discussion Paper Series 1504, Economics, The University of Manchester.
    15. Stephen G. Hall & George S. Tavlas & Lorenzo Trapani & Yongli Wang, 2025. "On the Detection of Structural Breaks: The Case of the Covid Shock," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 1042-1070, April.
    16. Molly C. Klanderman & Kathryn B. Newhart & Tzahi Y. Cath & Amanda S. Hering, 2020. "Fault isolation for a complex decentralized waste water treatment facility," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 931-951, August.
    17. Emilian DOBRESCU, 2022. "Macroeconomic Measurement of Expectations versus Reality," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-30, October.
    18. Tatsushi Oka & Pierre Perron, 2018. "Testing for common breaks in a multiple equations system," Monash Econometrics and Business Statistics Working Papers 3/18, Monash University, Department of Econometrics and Business Statistics.
    19. Alastair R. Hall & Sanggohn Han & Otilia Boldea, 2009. "Inference regarding multiple structural changes in linear models with endogenous regressors," Centre for Growth and Business Cycle Research Discussion Paper Series 125, Economics, The University of Manchester.
    20. Ghoshray, Atanu & Stamatogiannis, Michalis P., 2015. "Centurial evidence of breaks in the persistence of unemployment," Economics Letters, Elsevier, vol. 129(C), pages 74-76.
    21. Perron, Pierre & Yamamoto, Yohei & 山本, 庸平 & Zhou, Jing, 2019. "Testing Jointly for Structural Changes in the Error Variance and Coefficients of a Linear Regression Model," Discussion paper series HIAS-E-85, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    22. Gelman, Sergey & Burhop, Carsten, 2008. "Taxation, regulation and the information efficiency of the Berlin stock exchange, 1892–1913," European Review of Economic History, Cambridge University Press, vol. 12(1), pages 39-66, April.
    23. Ye Li & Pierre Perron, 2017. "Inference on locally ordered breaks in multiple regressions," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 289-353, March.
    24. Liao, Huei-Chu & Lee, Yi-Huey & Suen, Yu-Bo, 2008. "Electronic trading system and returns volatility in the oil futures market," Energy Economics, Elsevier, vol. 30(5), pages 2636-2644, September.
    25. Yu, Ping, 2015. "Consistency of the least squares estimator in threshold regression with endogeneity," Economics Letters, Elsevier, vol. 131(C), pages 41-46.
    26. Mohitosh Kejriwal, 2017. "A Robust Sequential Procedure for Estimating the Number of Structural Changes in Persistence," Purdue University Economics Working Papers 1303, Purdue University, Department of Economics.
    27. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2008. "Inference regarding multiple structural changes in linear models estimated via two stage least squares," MPRA Paper 9251, University Library of Munich, Germany, revised 20 Jun 2008.
    28. Petrenko, Victoria (Петренко, ВИктория) & Skrobotov, Anton (Скроботов, Антон) & Turuntseva, Maria (Турунцева, Мария), 2016. "Testing of Changes in Persistence and Their Effect on the Forecasting Quality [Тестирование Изменения Инерционности И Влияние На Качество Прогнозов]," Working Papers 542, Russian Presidential Academy of National Economy and Public Administration.
    29. Chen Fuqi & Nkurunziza Sévérien, 2014. "Constrained inference in multiple regression with structural changes," Statistics & Risk Modeling, De Gruyter, vol. 31(3-4), pages 237-257, December.
    30. Ping Yu & Peter C.B. Phillips, 2014. "Threshold Regression with Endogeneity," Cowles Foundation Discussion Papers 1966, Cowles Foundation for Research in Economics, Yale University.
    31. Karanasos, Menelaos & Paraskevopoulos, Alexandros & Magdalinos, Anastasios & Canepa, Alessandra, 2024. "A Unified Theory for Arma Models with Varying Coefficients: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202413, University of Turin.
    32. Mohitosh Kejriwal & Pierre Perron, 2006. "The Limit Distribution of the Estimates in Cointegrated Regression Models with Multiple Structural Changes," Boston University - Department of Economics - Working Papers Series WP2006-064, Boston University - Department of Economics.
    33. Syed A. Basher & Josep Lluis Carrión-i-Silvestre, 2008. "Price level convergence, purchasing power parity and multiple structural breaks: An application to US cities," Working Papers XREAP2008-08, Xarxa de Referència en Economia Aplicada (XREAP), revised Jul 2008.
    34. Rao, B. Bhaskara & Rao, Gyaneshwar, 2007. "Structural breaks and energy efficiency in Fiji," MPRA Paper 3258, University Library of Munich, Germany.
    35. Carmine Trecroci, 2014. "How Do Alphas and Betas Move? Uncertainty, Learning and Time Variation in Risk Loadings," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 257-278, April.
    36. Sévérien Nkurunziza & Pei Patrick Zhang, 2018. "Estimation and testing in generalized mean-reverting processes with change-point," Statistical Inference for Stochastic Processes, Springer, vol. 21(1), pages 191-215, April.
    37. Loredana Ureche-Rangau & Franck Speeg, 2011. "A simple method for variance shift detection at unknown time points," Economics Bulletin, AccessEcon, vol. 31(3), pages 2204-2218.
    38. Xiao Han & Nikolaos Sakkas & Jo Danbolt & Arman Eshraghi, 2022. "Persistence of investor sentiment and market mispricing," The Financial Review, Eastern Finance Association, vol. 57(3), pages 617-640, August.
    39. Casini, Alessandro & Perron, Pierre, 2021. "Continuous record Laplace-based inference about the break date in structural change models," Journal of Econometrics, Elsevier, vol. 224(1), pages 3-21.
    40. Manjola Tase, 2013. "Sectoral allocation, risk efficiency and the Great Moderation," Finance and Economics Discussion Series 2013-73, Board of Governors of the Federal Reserve System (U.S.).
    41. Hadhri, Sinda & Ftiti, Zied, 2019. "Commonality in liquidity among Middle East and North Africa emerging stock markets: Does it really matter?," Economic Systems, Elsevier, vol. 43(3).
    42. Luis F. Martins & Paulo M. M. Rodrigues, 2022. "Correction to: Tests for segmented cointegration: an application to US governments budgets," Empirical Economics, Springer, vol. 63(2), pages 601-601, August.
    43. Sévérien Nkurunziza, 2023. "Estimation and Testing in Multivariate Generalized Ornstein-Uhlenbeck Processes with Change-Points," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 351-400, February.
    44. Pierre Perron & Yohei Yamamoto, 2008. "Estimating and Testing Multiple Structural Changes in Models with Endogenous Regressors," Boston University - Department of Economics - Working Papers Series wp2008-017, Boston University - Department of Economics.
    45. Teryoshin, Yevgeniy, 2023. "Historical performance of rule-like monetary policy," Journal of International Money and Finance, Elsevier, vol. 130(C).
    46. Mai Ghannam & Sévérien Nkurunziza, 2024. "Change-point detection in a tensor regression model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(2), pages 609-630, June.
    47. Guglielmo Maria Caporale & Marinko Skare, 2014. "Long Memory in UK Real GDP, 1851-2013: An ARFIMA-FIGARCH Analysis," Discussion Papers of DIW Berlin 1395, DIW Berlin, German Institute for Economic Research.
    48. Pierre Perron & Yohei Yamamoto, 2011. "A Note on Estimating and Testing for Multiple Structural Changes in Models with Endogenous Regressors via 2SLS," Boston University - Department of Economics - Working Papers Series WP2011-054, Boston University - Department of Economics.
    49. Chulwoo Han & Abderrahim Taamouti, 2017. "Partial Structural Break Identification," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(2), pages 145-164, April.
    50. Dilem Yıldırım & Dilan Aydın, 2021. "One Crisis After Another: A Dynamic Unemployment Persistence Analysis For The Gips Countries," ERC Working Papers 2102, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    51. B. Bhaskara Rao, 2010. "Deterministic and stochastic trends in the time series models: a guide for the applied economist," Applied Economics, Taylor & Francis Journals, vol. 42(17), pages 2193-2202.
    52. Zhou, Jie & Sun, Mei & Han, Dun & Gao, Cuixia, 2021. "Analysis of oil price fluctuation under the influence of crude oil stocks and US dollar index — Based on time series network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    53. Nikolsko-Rzhevskyy, Alex & Papell, David H. & Prodan, Ruxandra, 2014. "Deviations from rules-based policy and their effects," Journal of Economic Dynamics and Control, Elsevier, vol. 49(C), pages 4-17.
    54. Cremaschini, Alessandro & Maruotti, Antonello, 2023. "A finite mixture analysis of structural breaks in the G-7 gross domestic product series," Research in Economics, Elsevier, vol. 77(1), pages 76-90.
    55. Somayeh Mardaneh, 2012. "How Do Oil Shocks A¤ect the Structural Stability of Hybrid New Keynesian Phillips Curve?," Discussion Papers in Economics 12/20, Division of Economics, School of Business, University of Leicester.
    56. Pierre Perron & Yohei Yamamoto, 2015. "Using OLS to Estimate and Test for Structural Changes in Models with Endogenous Regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 119-144, January.
    57. Cho, Dooyeon & Chun, Sungju, 2019. "Can structural changes in the persistence of the forward premium explain the forward premium anomaly?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 225-235.
    58. Fuqi Chen & Sévérien Nkurunziza, 2016. "A class of Stein-rules in multivariate regression model with structural changes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 83-102, March.
    59. Fuqi Chen & Rogemar Mamon & Sévérien Nkurunziza, 2018. "Inference for a change-point problem under a generalised Ornstein–Uhlenbeck setting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 807-853, August.

Chapters

  1. Denis Tkachenko & Zhongjun Qu, 2012. "Frequency Domain Analysis of Medium Scale DSGE Models with Application to Smets and Wouters (2007)," Advances in Econometrics, in: DSGE Models in Macroeconomics: Estimation, Evaluation, and New Developments, pages 319-385, Emerald Group Publishing Limited.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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