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Seasonality and Markov switching in an unobserved component time series model

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  • Rob Luginbuhl
  • Aart de Vos

Abstract

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. Copyright Springer-Verlag Berlin Heidelberg 2003

Suggested Citation

  • Rob Luginbuhl & Aart de Vos, 2003. "Seasonality and Markov switching in an unobserved component time series model," Empirical Economics, Springer, vol. 28(2), pages 365-386, April.
    • Handle: RePEc:spr:empeco:v:28:y:2003:i:2:p:365-386
    DOI: 10.1007/s001810200136
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    Cited by:

    1. Rob Luginbuhl, 2020. "Estimation of the Financial Cycle with a Rank-Reduced Multivariate State-Space Model," CPB Discussion Paper 409, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Antonio Matas-Mir & Denise R. Osborn & Marco J. Lombardi, 2008. "The effect of seasonal adjustment on the properties of business cycle regimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 257-278.
    3. Rob Luginbuhl, 2020. "Estimation of the Financial Cycle with a Rank-Reduced Multivariate State-Space Model," CPB Discussion Paper 409.rdf, CPB Netherlands Bureau for Economic Policy Analysis.

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