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Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator

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  • Konstantin, KHOLODILIN

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES))

Abstract

The analysis and prediction of the short-run economic dynamics, or the evolution of the business cycle, often require a construction of the composite economic indicator (CEI). This indicator may be endowed with nonlinear dynamics to take care of the possible asymmetries between different phases of the business cycle. This paper suggests using the smooth transition autoregression to model the CEI. The performance of this model is compared to the already classical CEI with regime switching. Both models turn out to produce statistically equally good results in terms of forecasting the business cycle turning points.

Suggested Citation

  • Konstantin, KHOLODILIN, 2002. "Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator," LIDAM Discussion Papers IRES 2002027, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    • Handle: RePEc:ctl:louvir:2002027
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    File URL: http://sites.uclouvain.be/econ/DP/IRES/2002-27.pdf
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    Cited by:

    1. Kholodilin Konstantin A., 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(6), pages 653-674, December.
    2. Konstantin A., Kholodilin, 2003. "Identifying and Forecasting the Turns of the Japanese Business Cycle," LIDAM Discussion Papers IRES 2003008, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    3. Konstantin A. Kholodilin, 2006. "Using the Dynamic Bi-Factor Model with Markov Switching to Predict the Cyclical Turns in the Large European Economies," Discussion Papers of DIW Berlin 554, DIW Berlin, German Institute for Economic Research.
    4. Catherine Doz & Anna Petronevich, 2016. "Dating Business Cycle Turning Points for the French Economy: An MS-DFM approach," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 481-538, Emerald Group Publishing Limited.
    5. Konstantin A. Kholodilin, 2005. "Forecasting the Turns of German Business Cycle: Dynamic Bi-factor Model with Markov Switching," Discussion Papers of DIW Berlin 494, DIW Berlin, German Institute for Economic Research.

    More about this item

    Keywords

    composite economic indicator; Markov switching; smooth transition autoregression; turning points; NBER dating; forecasting;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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