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

Listed author(s):
  • Sylvia Kaufmann

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

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.

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File URL: http://hdl.handle.net/10.1002/jae.1076
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File URL: http://qed.econ.queensu.ca:80/jae/2010-v25.2/
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 25 (2010)
Issue (Month): 2 ()
Pages: 309-344

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Handle: RePEc:jae:japmet:v:25:y:2010:i:2:p:309-344
DOI: 10.1002/jae.1076
Contact details of provider: Web page: http://www.interscience.wiley.com/jpages/0883-7252/

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