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The effect of seasonal adjustment on the properties of business cycle regimes

Author

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  • Antonio Matas-Mir

    (Directorate General Statistics, European Central Bank, Frankfurt, Germany)

  • Denise R. Osborn

    (Centre for Growth and Business Cycle Research, University of Manchester, Manchester, UK)

  • Marco J. Lombardi

    (Department of Economics, European University Institute, Fiesole, Italy)

Abstract

We study the impact of seasonal adjustment on the properties of business cycle expansion and recession regimes using analytical, simulation and empirical methods. Analytically, we show that the X-11 adjustment filter both reduces the magnitude of change at turning points and reduces the depth of recessions, with specific effects depending on the length of the recession. A Monte Carlo analysis using Markov-switching models confirms these properties, with particularly undesirable effects in delaying the recognition of the end of a recession. However, seasonal adjustment can help to clarify the true regime when this is well underway. These results continue to hold when a seasonally non-stationary process with regime-dependent mean is misspecified as one with deterministic seasonal effects. The empirical findings, based on four coincident US business cycle indicators, reinforce the analytical and simulation results by showing that seasonal adjustment leads to the identification of longer and shallower recessions than obtained using unadjusted data. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:jae:japmet:v:23:y:2008:i:2:p:257-278
    DOI: 10.1002/jae.980
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    2. A.S.M. Arroyo & A. de Juan Fern¨¢ndez, 2014. "Split-then-Combine Method for out-of-sample Combinations of Forecasts," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 3(1), pages 19-37, April.
    3. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    4. Monica Billio & Anna Petronevich, 2017. "Dynamical Interaction between Financial and Business Cycles," Post-Print hal-01692239, HAL.
    5. Michał Bernardelli & Monika Dędys, 2015. "Markov switching models in the analysis of business cycle synchronization," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 39, pages 213-228.
    6. Yoshihiro Ohtsuka, 2018. "Large Shocks and the Business Cycle: The Effect of Outlier Adjustments," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 143-178, April.
    7. Fomin, M., 2016. "Business cycles and acquisition policy: Analysis of M&A deals of metallurgical companies," Working Papers 6441, Graduate School of Management, St. Petersburg State University.

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