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Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data

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Abstract

The information contained in a large panel data set is used to date historical turning points of the Austrian business cycle and to forecast future ones. We estimate groups of series with similar time series dynamics and link the groups with a dynamic structure. The dynamic structure identifies a group of leading and a group of coincident series. Robust results across data vintages are obtained when series specific information is incorporated in the design of the prior group probability distribution. The results are consistent with common expectations, in particular the group of leading series includes Austrian confidence indicators and survey data, German survey indicators, some trade data, and, interestingly, the Austrian and the German stock market indices. The forecast evaluation confirms that the Markov switching panel with dynamic structure performs well when compared to other specifications.

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  • Sylvia Kaufmann, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank).
  • Handle: RePEc:onb:oenbwp:144
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    Cited by:

    1. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Interactions between eurozone and US booms and busts: A Bayesian panel Markov-switching VAR model," Working Papers 2013:17, Department of Economics, University of Venice "Ca' Foscari", revised 2014.
    2. Dolores Gadea-Rivas, M. & Gómez-Loscos, Ana & Bandrés, Eduardo, 2018. "Clustering regional business cycles," Economics Letters, Elsevier, vol. 162(C), pages 171-176.
    3. Roberto Casarin & Claudia Foroni & Massimiliano Marcellino & Francesco Ravazzolo, 2016. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," Working Papers 585, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. Sylvia Kaufmann, 2014. "K-state switching models with time-varying transition distributions – Does credit growth signal stronger effects of variables on inflation?," Working Papers 14.04, Swiss National Bank, Study Center Gerzensee.
    5. Francis, Neville & Owyang, Michael T. & Savascin, Özge, 2012. "An endogenously clustered factor approach to international business cycles," Working Papers 2012-014, Federal Reserve Bank of St. Louis, revised 10 Feb 2017.
    6. Roberto Casarin & Komla Mawulom Agudze & Monica Billio & Eric Girardin, 2014. "Growth-cycle phases in China�s provinces: A panel Markov-switching approach," Working Papers 2014:19, Department of Economics, University of Venice "Ca' Foscari".
    7. Kaufmann, Sylvia, 2015. "K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation?," Journal of Econometrics, Elsevier, vol. 187(1), pages 82-94.
    8. Gomez-Loscos, Ana & Gadea, M. Dolores & Bandres, Eduardo, 2018. "Business cycle patterns in European regions," MPRA Paper 83964, University Library of Munich, Germany.
    9. Sylvia Kaufmann, 2011. "K-state switching models with endogenous transition distributions," Working Papers 2011-13, Swiss National Bank.
    10. Hernández-Murillo, Rubén & Owyang, Michael T. & Rubio, Margarita, 2017. "Clustered housing cycles," Regional Science and Urban Economics, Elsevier, vol. 66(C), pages 185-197.
    11. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse seemingly unrelated regression model (SUR)," Working Papers 2016:20, Department of Economics, University of Venice "Ca' Foscari".
    12. James D. Hamilton & Michael T. Owyang, 2012. "The Propagation of Regional Recessions," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 935-947, November.
    13. Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
    14. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised May 2018.
    15. Adrian Pagan & Don Harding, 2011. "Econometric Analysis and Prediction of Recurrent Events," NCER Working Paper Series 75, National Centre for Econometric Research.
    16. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.

    More about this item

    Keywords

    Bayesian clustering; parameter heterogeneity; latent dynamic structure; Markov switching; panel data; turning points.;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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