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Unobserved-Component Time-Series Models with Markov- Switching Heteroskedasticity: Changes in Regimes and the Link between Inflation Rates and Inflation Uncertainty

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  • Kim, C.J.

Abstract

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.
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Suggested Citation

  • Kim, C.J., 1992. "Unobserved-Component Time-Series Models with Markov- Switching Heteroskedasticity: Changes in Regimes and the Link between Inflation Rates and Inflation Uncertainty," Papers 92-1, York (Canada) - Department of Economics.
    • Handle: RePEc:fth:yorkca:92-1
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