Unobserved-Component Time-Series Models with Markov- Switching Heteroskedasticity: Changes in Regimes and the Link between Inflation Rates and Inflation Uncertainty
AbstractIn 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 InfoPaper provided by York (Canada) - Department of Economics in its series Papers with number 92-1.
Length: 18 pages
Date of creation: 1992
Date of revision:
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Postal: YORK UNIVERSITY, DEPARTMENT OF ECONOMICS, 4700 KEELE ST-NORTH YORK ONTARIO H3J 1P3 CANADA.
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Web page: http://dept.econ.yorku.ca/
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economic models ; economic theory ; prices;
Other versions of this item:
- Kim, Chang-Jin, 1993. "Unobserved-Component Time Series Models with Markov-Switching Heteroscedasticity: Changes in Regime and the Link between Inflation Rates and Inflation Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 11(3), pages 341-49, July.
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