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Choosing information variables for transition probabilities in a time-varying transition probability Markov switching model

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  • Andrew J. Filardo

Abstract

This paper discusses a practical estimation issue for time-varying transition probability (TVTP) Markov switching models. Time-varying transition probabilities allow researchers to capture important economic behavior that may be missed using constant (or fixed) transition probabilities. Despite its use, Hamilton?s (1989) filtering method for estimating fixed transition probability Markov switching models may not apply to TVTP models. This paper provides a set of sufficient conditions to justify the use of Hamilton?s method for TVTP models. In general, the information variables that govern time-variation in the transition probabilities must be conditionally uncorrelated with the state of the Markov process.

Suggested Citation

  • Andrew J. Filardo, 1998. "Choosing information variables for transition probabilities in a time-varying transition probability Markov switching model," Research Working Paper 98-09, Federal Reserve Bank of Kansas City.
    • Handle: RePEc:fip:fedkrw:98-09
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    References listed on IDEAS

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    1. Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-434, March.
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    6. Engel, Charles & Hakkio, Craig S, 1996. "The Distribution of Exchange Rates in the EMS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 1(1), pages 55-67, January.
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    11. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
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    Cited by:

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    3. JdD Tena & E. Otranto, 2008. "A Realistic Model for Official Interest Rates," Working Paper CRENoS 200802, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    4. Grand Nathalie & Dropsy Vincent, 2005. "Exchange Rate And Inflation Targeting In Morocco And Tunisia," Macroeconomics 0507018, University Library of Munich, Germany.
    5. Park, Jin Suk & Shi, Yukun, 2017. "Hedging and speculative pressures and the transition of the spot-futures relationship in energy and metal markets," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 176-191.
    6. Beine, Michel & Laurent, Sebastien & Lecourt, Christelle, 2003. "Official central bank interventions and exchange rate volatility: Evidence from a regime-switching analysis," European Economic Review, Elsevier, vol. 47(5), pages 891-911, October.
    7. Bram van Os & Dick van Dijk, 2020. "Accelerating Peak Dating in a Dynamic Factor Markov-Switching Model," Tinbergen Institute Discussion Papers 20-057/VI, Tinbergen Institute, revised 14 Dec 2020.
    8. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2016. "Maximum Likelihood Estimation in Possibly Misspeci ed Dynamic Models with Time-Inhomogeneous Markov Regimes," Department of Economics Working Papers 2016_04, Universidad Torcuato Di Tella.
    9. Carlos Ulibarri, 2005. "Bayesian Learning from Arts Goods? – A Comment," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 29(2), pages 137-141, May.
    10. Erlandsson, Ulf, 2005. "Transition Variables in the Markov-switching Model: Some Small Sample Properties," Working Papers 2005:25, Lund University, Department of Economics.
    11. Sapio, Alessandro & Spagnolo, Nicola, 2016. "Price regimes in an energy island: Tacit collusion vs. cost and network explanations," Energy Economics, Elsevier, vol. 55(C), pages 157-172.

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    time series analysis; Estimation theory;

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