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Non-Markovian Regime Switching with Endogenous States and Time-Varying State Strengths

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  • Michael Dueker

Abstract

This article presents a non-Markovian regime switching model in which the regime states depend on the sign of an autoregressive latent variable. The magnitude of the latent variable indexes the `strength' of the state or how deeply the system is embedded in the current regime. The autoregressive nature of this non-Markovian regime switching implies time-varying state transition probabilities, even in the absence of an exogenous covariate. Furthermore, with time-varying regime strengths, the expected duration of a regime is time-varying. In this framework, it is natural to allow the autoregressive latent variable to be endogenous so that regimes are determined jointly with the observed data. We apply the model to GDP growth, as in Hamilton (1989), Albert and Chib (1993) and Filardo and Gordon (1998) to illustrate the relation of the regimes to NBER-dated recessions and the time-varying expected durations of regimes

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 Latin American Meetings with number 34.

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Date of creation: 11 Aug 2004
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Publication status: published in The B.E. Journal of Macroeconomics, 2011, vol. 11, issue 1.
Handle: RePEc:ecm:latm04:34

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Keywords: Regime switching; Markov Chain Monte Carlo;

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References

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  1. Andrew J. Filardo & Stephen F. Gordon, 1993. "Business cycle durations," Research Working Paper 93-11, Federal Reserve Bank of Kansas City.
  2. Tiemen Woutersen & Robert M. de Jong, 2004. "Dynamic time series binary choice," Econometric Society 2004 North American Summer Meetings 365, Econometric Society.
  3. Pok-sang Lam, 2004. "A Markov-Switching Model Of Gnp Growth With Duration Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(1), pages 175-204, 02.
  4. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August.
  5. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
  6. Christopher A. Sims & Tao Zha, 2005. "Were There Regime Switches in U.S. Monetary Policy?," Working Papers 92, Princeton University, Department of Economics, Center for Economic Policy Studies..
  7. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
  8. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
  9. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
  10. 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 357-84, March.
  11. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
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Cited by:
  1. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combination Schemes for Turning Point Predictions," Tinbergen Institute Discussion Papers 11-123/4, Tinbergen Institute.
  2. Mark W. French, 2005. "A nonlinear look at trend MFP growth and the business cycle: result from a hybrid Kalman/Markov switching model," Finance and Economics Discussion Series 2005-12, Board of Governors of the Federal Reserve System (U.S.).

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