Unobserved-Component Time Series Models With Markov-Switching Heteroscedasticity

Changes in Regime and the Link Between Inflation Rates and Inflation Uncertainty

Bibliographic Data

ID19418571
AuthorsChang-Jin Kim (Korea University), Chang‐Jin Kim (0000-0002-4600-9962, corresponding author)
Year1993
Volume11
Issue3
Pages341-349
Publication date1993-07-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.1993.10509962
OpenAlexW2044541714
LanguageEN
Citations received3
References cited22

In this article, I first extend the standard unobserved-component time series model to include Hamilton's Markov-switching heteroscedasticity. This will provide an alternative to the unobserved-component model with autoregressive conditional heteroscedasticity, as developed by Harvey, Ruiz, and Sentana and by Evans and Wachtel. I then apply a generalized version of the model to investigate the link between inflation and its uncertainty (U.S. data, gross national product deflator, 1958:1-1990:4). I assume that inflation consists of a stochastic trend (random-walk) component and a stationary autoregressive component, following Ball and Cecchetti, and a four-state model of U.S. inflation rate is specified. By incorporating regime shifts in both mean and variance structures, I analyze the interaction of mean and variance over long and short horizons. The empirical results show that inflation is costly because higher inflation is associated with higher long-run uncertainty

Autoregressive conditional heteroskedasticity · Autoregressive model · Conditional variance · Econometrics · Economics · Heteroscedasticity · Markov chain · Statistics · Stochastic volatility · Economic theories and models · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact

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