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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
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    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. 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.
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    5. 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.
    6. 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.
    7. 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.
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    More about this item

    Keywords

    endogeneity; Markov switching model; joint estimation procedure; two-step estimation procedure; MCMC estimation procedure; consumption model;
    All these 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

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