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Choosing Information Variables for Transition Probabilities In a Time-Varying Transition Probability Markov Switching ModelAndrew 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. Andrew J. Filardo is a senior economist at the Federal Reserve Bank of
Kansas City. The views expressed in this paper are those of the author and do not
necessarily represent those of the Federal Reserve Bank of Kansas City or the Federal
Reserve System.
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