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Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes

  • Fady Barsoum

    ()

    (Department of Economics, University of Konstanz, Germany)

  • Sandra Stankiewicz

    ()

    (Department of Economics, University of Konstanz, Germany)

For modelling mixed-frequency data with business cycle pattern we introduce the Markovswitching Mixed Data Sampling model with unrestricted lag polynomial (MS-U-MIDAS). Usually models of the MIDAS-class use lag polynomials of a specific function, which impose some structure on the weights of regressors included in the model. This may deteriorate the predictive power of the model if the imposed structure differs from the data generating process. When the difference between the available data frequencies is small and there is no risk of parameter proliferation, using an unrestricted lag polynomial might not only simplify the model estimation, but also improve its forecasting performance. We allow the parameters of the MIDAS model with unrestricted lag polynomial to change according to a Markov-switching scheme in order to account for the business cycle pattern observed in many macroeconomic variables. Thus we combine the unrestricted MIDAS with a Markov-switching approach and propose a new Markov-switching MIDAS model with unrestricted lag polynomial (MS-U-MIDAS). We apply this model to a large dataset with the help of factor analysis. Monte Carlo experiments and an empirical forecasting comparison carried out for the U.S. GDP growth show that the models of the MS-UMIDAS class exhibit similar or better nowcasting and forecasting performance than their counterparts with restricted lag polynomials.

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Paper provided by Department of Economics, University of Konstanz in its series Working Paper Series of the Department of Economics, University of Konstanz with number 2013-10.

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Length: 44 pages
Date of creation: 08 May 2013
Date of revision:
Handle: RePEc:knz:dpteco:1310
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  1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
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  13. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, 08.
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  16. Emre Alper, C. & Fendoglu, Salih & Saltoglu, Burak, 2012. "MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets," Economics Letters, Elsevier, vol. 117(2), pages 528-532.
  17. Charles Engel, 1991. "Can the Markov switching model forecast exchange rates?," Research Working Paper 91-04, Federal Reserve Bank of Kansas City.
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