IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v18y2014i4p16n2.html
   My bibliography  Save this article

An extensive study on Markov switching models with endogenous regressors

Author

Listed:
  • Wang Xia

    (School of Management, University of Chinese Academy of Sciences, Beijing, 100190, China)

  • Shang Yuhuang

    (Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian, 361005, China)

  • Zheng Tingguo

    (Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian, 361005, China Economic Building (WISE), Siming South Road, Xiamen City, Fujian Province, China)

Abstract

This paper extends Kim’s (Kim, C.-J. 2004. “Markov-Switching Models with Endogenous Explanatory Variables.” Journal of Econometrics 122: 127–136; Kim, C.-J. 2009. “Markov-Switching Models with Endogenous Explanatory Variables II: A Two-Step MLE Procedure.” Journal of Econometrics 148: 46–55.) studies on Markov switching models with endogenous regressors by considering the time-varying relationship between endogenous regressors and instrumental variables. To deal with the endogenous problem, we introduce three estimation methods, e.g., a joint estimation procedure, a two-step estimation procedure and a MCMC estimation procedure. Although the joint estimation procedure provides us with a direct estimator via Kim’s (Kim, C.-J. 1994. “Dynamic Linear Models with Markov-Switching.” Journal of Econometrics 60: 1–22.) approximation, it is not always feasible due to the “curse of dimensionality” problem. In this case, we consider the two-step estimation procedure and the MCMC estimation procedure. Our Monte Carlo experiments show that three estimation procedures are in general feasible and robust. Although the two-step estimation procedure is not as efficient as the MCMC estimation procedure, they both perform better than the joint estimation procedure. In an application to the Campbell and Mankiw’s (1989) consumption model, we document the robustness of our three estimation procedures and verify the significant regime switching behavior of this model.

Suggested Citation

  • Wang Xia & Shang Yuhuang & Zheng Tingguo, 2014. "An extensive study on Markov switching models with endogenous regressors," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(4), pages 403-418, September.
    • Handle: RePEc:bpj:sndecm:v:18:y:2014:i:4:p:16:n:2
    DOI: 10.1515/snde-2012-0071
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/snde-2012-0071
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/snde-2012-0071?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Chang-Jin, 2009. "Markov-switching models with endogenous explanatory variables II: A two-step MLE procedure," Journal of Econometrics, Elsevier, vol. 148(1), pages 46-55, January.
    2. John Y. Campbell & N. Gregory Mankiw, 1989. "Consumption, Income, and Interest Rates: Reinterpreting the Time Series Evidence," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 185-246, National Bureau of Economic Research, Inc.
    3. Gert Peersman & Lorenzo Pozzi, 2008. "Business Cycle Fluctuations and Excess Sensitivity of Private Consumption," Economica, London School of Economics and Political Science, vol. 75(299), pages 514-523, August.
    4. Martin Sola & Zacharias Psaradakis & Fabio Spagnolo, 2005. "Testing the unbiased forward exchange rate hypothesis using a Markov switching model and instrumental variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 423-437.
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    6. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    7. Kim Chang-Jin & Kim Yunmi, 2008. "Is the Backward-Looking Component Important in a New Keynesian Phillips Curve?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-20, September.
    8. Psaradakis Zacharias & Sola Martin & Spagnolo Fabio, 2006. "Instrumental-Variables Estimation in Markov Switching Models with Endogenous Explanatory Variables: An Application to the Term Structure of Interest Rates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(2), pages 1-31, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jinho Bae & Chang-Jin Kim & Dong Kim, 2012. "The evolution of the monetary policy regimes in the U.S," Empirical Economics, Springer, vol. 43(2), pages 617-649, October.
    2. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    3. Valentina Aprigliano & Danilo Liberati, 2021. "Using Credit Variables to Date Business Cycle and to Estimate the Probabilities of Recession in Real Time," Manchester School, University of Manchester, vol. 89(S1), pages 76-96, September.
    4. Calvet, Laurent E. & Fisher, Adlai J., 2007. "Multifrequency news and stock returns," Journal of Financial Economics, Elsevier, vol. 86(1), pages 178-212, October.
    5. Owyang, Michael T. & Piger, Jeremy M. & Wall, Howard J. & Wheeler, Christopher H., 2008. "The economic performance of cities: A Markov-switching approach," Journal of Urban Economics, Elsevier, vol. 64(3), pages 538-550, November.
    6. Shyh-Wei Chen, 2008. "Identifying US turning points revisited: the panel model with the regime switching approach," Applied Economics Letters, Taylor & Francis Journals, vol. 15(11), pages 893-897.
    7. Chun-Chang Lee & Chih-Min Liang & Hsing-Jung Chou, 2013. "Identifying Taiwan real estate cycle turning points- An application of the multivariate Markov-switching autoregressive Model," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 3(2), pages 1-1.
    8. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
    9. Chang, Yoosoon & Maih, Junior & Tan, Fei, 2021. "Origins of monetary policy shifts: A New approach to regime switching in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    10. Liu, Wen-Hsien & Chyi, Yih-Luan, 2006. "A Markov regime-switching model for the semiconductor industry cycles," Economic Modelling, Elsevier, vol. 23(4), pages 569-578, July.
    11. Sergey V. Smirnov & Nikolay V. Kondrashov & Anna V. Petronevich, 2017. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 53-73, May.
    12. Grace Lee, 2011. "Aggregate shocks decomposition for eight East Asian countries," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 16(2), pages 215-232.
    13. Cruz-Rodríguez, Alexis, 2004. "Un análisis del ciclo económico de la República Dominicana bajo cambios de régimen [Analysis of business cycle of the Dominican Republic using Markov Switching model]," MPRA Paper 54352, University Library of Munich, Germany.
    14. Balcilar, Mehmet & Gupta, Rangan & Miller, Stephen M., 2015. "Regime switching model of US crude oil and stock market prices: 1859 to 2013," Energy Economics, Elsevier, vol. 49(C), pages 317-327.
    15. Vitor Castro, 2015. "The Portuguese business cycle: chronology and duration dependence," Empirical Economics, Springer, vol. 49(1), pages 325-342, August.
    16. Jaehee Kim & Sooyoung Cheon, 2010. "A Bayesian regime‐switching time‐series model," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 365-378, September.
    17. Yoosoon Chang & Fei Tan & Xin Wei, 2018. "State Space Models with Endogenous Regime Switching," CAEPR Working Papers 2018-012, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    18. Keisuke Kondo, 2022. "Spatial dependence in regional business cycles: evidence from Mexican states," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-46, December.
    19. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2005. "Business Cycle Phases in U.S. States," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 604-616, November.
    20. Otilia Boldea & Alastair R. Hall, 2013. "Testing structural stability in macroeconometric models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 9, pages 206-228, Edward Elgar Publishing.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sndecm:v:18:y:2014:i:4:p:16:n:2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.