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Measuring business cycles with a dynamic Markov switching factor model: an assessment using Bayesian simulation methods

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  • SYLVIA KAUFMANN

Abstract

A Markov switching common factor is used to drive a dynamic factor model for important macroeconomic variables in eight countries. Bayesian estimation of the model is based on Markov chain Monte Carlo simulation methods which yield inferences about the unobservable path of the common factor, the latent variable of the state process and all model parameters. Additionally, simulation based filtering provides us with samples from the prediction density that can be used for model diagnostics and specification tests. The mean posterior state probabilities are used to date business cycle turning points that follow quite closely previous datings reported in the literature. Moreover, we test the Markov switching against a no-switching specification by means of a Bayes factor. The evidence proves to be quite favorable for the Markov switching model.

Suggested Citation

  • Sylvia Kaufmann, 2000. "Measuring business cycles with a dynamic Markov switching factor model: an assessment using Bayesian simulation methods," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 39-65.
    • Handle: RePEc:ect:emjrnl:v:3:y:2000:i:1:p:39-65
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    Citations

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    Cited by:

    1. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
    2. Sylvia Kaufmann, 2002. "Is there an asymmetric effect of monetary policy over time? A Bayesian analysis using Austrian data," Empirical Economics, Springer, vol. 27(2), pages 277-297.
    3. Sylvia Kaufmann & Sylvia Frühwirth‐Schnatter, 2002. "Bayesian analysis of switching ARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 425-458, July.
    4. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 227-244, October.
    5. 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.
    6. Monica Billio & Laurent Ferrara & Dominique Guegan & Gian Luigi Mazzi, 2009. "Evaluation of Nonlinear time-series models for real-time business cycle analysis of the Euro," Documents de travail du Centre d'Economie de la Sorbonne 09053, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Monica Billio & Roberto Casarin & Matteo Iacopini, 2018. "Bayesian Markov Switching Tensor Regression for Time-varying Networks," Working Papers 2018:14, Department of Economics, University of Venice "Ca' Foscari".
    8. Hauzenberger Niko & Huber Florian & Pfarrhofer Michael & Zörner Thomas O., 2021. "Stochastic model specification in Markov switching vector error correction models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
    9. Sungyup Chung, 2016. "Assessing the regional business cycle asymmetry in a multi-level structure framework: a study of the top 20 US MSAs," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 229-252, January.
    10. Michael Funke & Harm Bandholz, 2003. "In search of leading indicators of economic activity in Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 277-297.
    11. Sylvia Frühwirth-Schnatter, 2001. "Fully Bayesian Analysis of Switching Gaussian State Space Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(1), pages 31-49, March.
    12. 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.
    13. 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).
    14. Wang, Jin-ming & Gao, Tie-mei & McNown, Robert, 2009. "Measuring Chinese business cycles with dynamic factor models," Journal of Asian Economics, Elsevier, vol. 20(2), pages 89-97, March.
    15. Louise Holm, 2016. "The Swedish business cycle, 1969-2013," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(2), pages 1-22.
    16. Aneta Wlodarczyk & Marcin Zawada, 2008. "Markov-Switching Models for the Prices of Electric Energy on the Energy Stock Market in Poland," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 8, pages 171-178.
    17. Rokas Gylys & Jonas Šiaulys, 2020. "Estimation of Uncertainty in Mortality Projections Using State-Space Lee-Carter Model," Mathematics, MDPI, vol. 8(7), pages 1-23, June.
    18. Chuku Chuku & Paul Middleditch, 2020. "Characterizing Monetary and Fiscal Policy Rules and Interactions when Commodity Prices Matter," Manchester School, University of Manchester, vol. 88(3), pages 373-404, June.
    19. 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.
    20. Sungyup Chung, 2016. "Assessing the regional business cycle asymmetry in a multi-level structure framework: a study of the top 20 US MSAs," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 229-252, January.
    21. Penelope A. Smith & Peter M. Summers, 2004. "Identification and normalization in Markov switching models of \"business cycles\"," Research Working Paper RWP 04-09, Federal Reserve Bank of Kansas City.
    22. 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.

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