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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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.

Original languageEnglish
Pages (from-to)341-349
Number of pages9
JournalJournal of Business and Economic Statistics
Issue number3
Publication statusPublished - 1993



  • Long-run inflation uncertainty
  • Markov-switching heteroscedasticity
  • Quasioptimal filter
  • Short-run inflation uncertainty
  • Unobserved-component model

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty
  • Social Sciences (miscellaneous)

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