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A new approach to model regime switching

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  • Chang, Yoosoon
  • Choi, Yongok
  • Park, Joon Y.

Abstract

This paper introduces a new approach to model regime switching using an autoregressive latent factor, which determines regimes depending upon whether it takes a value above or below some threshold level. In our approach, the latent factor is allowed to be correlated with the innovation to the observed time series. If the latent factor becomes exogenous, our approach reduces to the conventional Markov switching. We develop a modified Markov switching filter to estimate the mean and volatility models with Markov switching that are frequently analyzed, and find that the presence of endogeneity in regime switching is indeed strong and ubiquitous.

Suggested Citation

  • Chang, Yoosoon & Choi, Yongok & Park, Joon Y., 2017. "A new approach to model regime switching," Journal of Econometrics, Elsevier, vol. 196(1), pages 127-143.
    • Handle: RePEc:eee:econom:v:196:y:2017:i:1:p:127-143
    DOI: 10.1016/j.jeconom.2016.09.005
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    References listed on IDEAS

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    4. Kim, C.-J.Chang-Jin, 2004. "Markov-switching models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 122(1), pages 127-136, September.
    5. Kim, Chang-Jin & Nelson, Charles R. & Startz, Richard, 1998. "Testing for mean reversion in heteroskedastic data based on Gibbs-sampling-augmented randomization1," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 131-154, June.
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    More about this item

    Keywords

    Regime switching model; Latent factor; Endogeneity; Mean reversion; Leverage effect; Maximum likelihood estimation; Markov chain;
    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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