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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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  • Sylvia Kaufmann

    (Oesterreichische Nationalbank, Vienna, Austria, and University of Basel, Switzerland)

Abstract

The information contained in a large panel dataset is used to date historical turning points 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 forecast evaluation confirms that the Markov switching panel with dynamic structure performs well when compared to other specifications. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    • Handle: RePEc:jae:japmet:v:25:y:2010:i:2:p:309-344
    DOI: 10.1002/jae.1076
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    References listed on IDEAS

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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. 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.
    3. Neville Francis & Michael T. Owyang & Ozge Savascin, 2017. "An endogenously clustered factor approach to international business cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1261-1276, November.
    4. 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".
    5. repec:eee:econom:v:212:y:2019:i:1:p:97-115 is not listed on IDEAS
    6. 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.
    7. Hernández-Murillo, Rubén & Owyang, Michael T. & Rubio, Margarita, 2017. "Clustered housing cycles," Regional Science and Urban Economics, Elsevier, vol. 66(C), pages 185-197.
    8. 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.
    9. Adrian Pagan & Don Harding, 2011. "Econometric Analysis and Prediction of Recurrent Events," NCER Working Paper Series 75, National Centre for Econometric Research.
    10. 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.
    11. Dolores Gadea-Rivas, M. & Gómez-Loscos, Ana & Bandrés, Eduardo, 2018. "Clustering regional business cycles," Economics Letters, Elsevier, vol. 162(C), pages 171-176.
    12. Gomez-Loscos, Ana & Gadea, M. Dolores & Bandres, Eduardo, 2018. "Business cycle patterns in European regions," MPRA Paper 83964, University Library of Munich, Germany.
    13. Sylvia Kaufmann, 2011. "K-state switching models with endogenous transition distributions," Working Papers 2011-13, Swiss National Bank.
    14. 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".
    15. 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.
    16. repec:gam:jecnmx:v:7:y:2019:i:1:p:8-:d:212762 is not listed on IDEAS
    17. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    18. Michael T. Owyang & Jeremy M. Piger & Daniel Soques, 2019. "Contagious Switching," Working Papers 2019-14, Federal Reserve Bank of St. Louis.
    19. 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

    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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