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Alternative Regime Switching Models for Forecasting Inflation

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Author Info
Bidarkota, Prasad V

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Abstract

US inflation appears to undergo shifts in its mean level and variability. We evaluate the performance of three useful models for capturing such shifts. The models studied are the Markov switching models, state space models with heavy-tailed errors, and state space models with compound error distributions. Our study shows that all three models have very similar performance when evaluated in terms of the mean squared or mean absolute forecast errors. However, the latter two models are considerably more parsimonious, and easily beat the more profligately parameterized Markov switching models in terms of model selection criteria, such as the AIC or the SBC. Thus, these may serve as useful continuous alternatives to the popular discrete Markov switching models for capturing shifts in time series. Copyright © 2001 by John Wiley & Sons, Ltd.

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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 20 (2001)
Issue (Month): 1 (January)
Pages: 21-35
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Handle: RePEc:jof:jforec:v:20:y:2001:i:1:p:21-35

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Marie Bessec & Othman Bouabdallah, 2005. "What causes the forecasting failure of Markov-Switching models? A Monte Carlo study," Econometrics 0503018, EconWPA. [Downloadable!]
    Other versions:
  2. A. Espasa & P: Poncela & E. Senra, 2002. "Forecasting Monthly Us Consumer Price Indexes Through A Disaggregated I(2) Analysis," Statistics and Econometrics Working Papers ws020301, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  3. Juan Ayuso & Graciela L. Kaminsky & David López-Salido, 2003. "Inflation regimes and stabilisation policies: Spain 1962-2001," Investigaciones Economicas, Fundación SEPI, vol. 27(3), pages 615-631, September. [Downloadable!]
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