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

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  • Kim, Chang-Jin

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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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 11 (1993)
Issue (Month): 3 (July)
Pages: 341-49

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Handle: RePEc:bes:jnlbes:v:11:y:1993:i:3:p:341-49

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