Abstract
If the parameters of a time-series process are subject to change over time, then a full description of the data-generating process must include a specification of the probability law governing these changes, for example, postulating that the parameters evolve according to the realization of an unobserved Markov chain. This article describes classical and Bayesian algorithms for estimation and inference in such models and discusses some of the issues that arise in particular cases such as GARCH and state-space models.
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Bibliography
Albert, J., and S. Chib. 1993. Bayes inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts. Journal of Business and Economic Statistics 11: 1–15.
Ang, A., and G. Bekaert. 2002a. International asset allocation with regime shifts. Review of Financial Studies 15: 1137–1187.
Ang, A., and G. Bekaert. 2002b. Regime switches in interest rates. Journal of Business and Economic Statistics 20: 163–182.
Baum, L., E. Petrie, G. Soules, and N. Weiss. 1980. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics 41: 164–171.
Calvet, L., and A. Fisher. 2004. How to forecast long-run volatility: Regime-switching and the estimation of multifractal processes. Journal of Financial Econometrics 2: 49–83.
Carrasco, M., L. Hu, and W. Ploberger. 2004. Optimal test for Markov switching. Working paper. University of Rochester.
Cerra, V., and S. Saxena. 2005. Did output recover from the Asian crisis? IMF Staff Papers 52: 1–23.
Chauvet, M., and J. Hamilton. 2006. Dating business cycle turning points. In Nonlinear time series analysis of business cycles, ed. C. Milas, P. Rothman, and D. van Dijk. Amsterdam: Elsevier.
Chib, S. 1998. Estimation and comparison of multiple change-point models. Journal of Econometrics 86: 221–241.
Cosslett, S., and L.-F. Lee. 1985. Serial correlation in discrete variable models. Journal of Econometrics 27: 79–97.
Dai, Q., K. Singleton, and W. Yang. 2003. Regime shifts in a dynamic term structure model of U.S. Treasury bonds. Working paper, Stanford University.
Davig, T. 2004. Regime-switching debt and taxation. Journal of Monetary Economics 51: 837–859.
Diebold, F., J.-H. Lee, and G. Weinbach. 1994. Regime switching with time-varying transition probabilities. In Nonstationary time series analysis and cointegration, ed. C. Hargreaves. Oxford: Oxford University Press.
Dueker, M. 1997. Markov switching in GARCH processes and mean-reverting stockmarket volatility. Journal of Business and Economic Statistics 15: 26–34.
Filardo, A. 1994. Business cycle phases and their transitional dynamics. Journal of Business and Economic Statistics 12: 299–308.
Filardo, A., and S. Gordon. 1998. Business cycle durations. Journal of Econometrics 85: 99–123.
Francq, C., and J.-M. Zakoïan. 2001. Stationarity of multivariate Markov-switching ARMA models. Journal of Econometrics 102: 339–364.
Garcia, R. 1998. Asymptotic null distribution of the likelihood ratio test in Markov switching models. International Economic Review 39: 763–788.
Garcia, R., R. Luger, and E. Renault. 2003. Empirical assessment of an intertemporal option pricing model with latent variables. Journal of Econometrics 116: 49–83.
Goldfeld, S., and R. Quandt. 1973. A Markov model for switching regressions. Journal of Econometrics 1: 3–16.
Gray, S. 1996. Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics 42: 27–62.
Haas, M., S. Mittnik, and M. Paolella. 2004. A new approach to Markov-switching GARCH models. Journal of Financial Econometrics 2: 493–530.
Hamilton, J. 1988. Rational-expectations econometric analysis of changes in regime: An investigation of the term structure of interest rates. Journal of Economic Dynamics and Control 12: 385–423.
Hamilton, J. 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57: 357–384.
Hamilton, J. 1994. Time series analysis. Princeton: Princeton University Press.
Hamilton, J. 1996. Specification testing in Markov-switching time-series models. Journal of Econometrics 70: 127–157.
Hamilton, J. 2005. What’s real about the business cycle? Federal Reserve Bank of St. Louis Review 87: 435–452.
Hamilton, J., and G. Lin. 1996. Stock market volatility and the business cycle. Journal of Applied Econometrics 11: 573–593.
Hamilton, J., and G. Perez-Quiros. 1996. What do the leading indicators lead? Journal of Business 69: 27–49.
Hamilton, J., and R. Susmel. 1994. Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics 64: 307–333.
Hansen, B. 1992. The likelihood ratio test under non-standard conditions. Journal of Applied Econometrics 7: S61–S82. Erratum, 11(1996), 195–198.
Jeanne, O., and P. Masson. 2000. Currency crises, sunspots, and Markov-switching regimes. Journal of International Economics 50: 327–350.
Juang, B.-H., and L. Rabiner. 1985. Mixture autoregressive hidden Markov models for speech signals. IEEE Transactions on Acoustics, Speech, and Signal Processing 30: 1404–1413.
Kim, C. 1994. Dynamic linear models with Markov-switching. Journal of Econometrics 60: 1–22.
Kim, C., and C. Nelson. 1999. State-space models with regime switching. Cambridge, MA: MIT Press.
Koop, G., and S. Potter. 1999. Bayes factors and nonlinearity: Evidence from economic time series. Journal of Econometrics 88: 251–281.
Krolzig, H.-M. 1997. Markov-switching vector autoregressions: Modelling, statistical inference, and application to business cycle analysis. Berlin: Springer.
Lindgren, G. 1978. Markov regime models for mixed distributions and switching regressions. Scandinavian Journal of Statistics 5: 81–91.
Peria, M. 2002. A regime-switching approach to the study of speculative attacks: A focus on EMS crises. In Advances in Markov-switching models, ed. J. Hamilton and B. Raj. Heidelberg: Physica Verlag.
Poritz, A. 1982. Linear predictive hidden Markov models and the speech signal. Acoustics, Speech and Signal Processing, IEEE Conference on ICASSP ’82 7: 1291–1294.
Rabiner, L. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77: 257–286.
Sims, C., and T. Zha. 2006. Were there switches in U.S. monetary policy? American Economic Review 96: 54–81.
Timmermann, A. 2000. Moments of Markov switching models. Journal of Econometrics 96: 75–111.
Tjøstheim, D. 1986. Some doubly stochastic time series models. Journal of Time Series Analysis 7: 51–72.
Yang, M. 2000. Some properties of vector autoregressive processes with Markov-switching coefficients. Econometric Theory 16: 23–43.
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Hamilton, J.D. (2018). Regime Switching Models. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2459
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2459
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