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Forecasting GDP over the business cycle in a multi-frequency and data-rich environment

Listed author(s):
  • Bessec, M.
  • Bouabdallah, O.

This paper merges two specifications developed recently in the forecasting literature: the MS-MIDAS model introduced by Guérin and Marcellino (2011) and the MIDAS-factor model considered in Marcellino and Schumacher (2010). The MS-factor MIDAS model (MS-FaMIDAS) that we introduce incorporates the information provided by a large data-set, takes into account mixed frequency variables and captures regime-switching behaviors. Monte Carlo simulations show that this new specification tracks the dynamics of the process quite well and predicts the regime switches successfully, both in sample and out-of-sample. We apply this new model to US data from 1959 to 2010 and detect properly the US recessions by exploiting the link between GDP growth and higher frequency financial variables.

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File URL: https://publications.banque-france.fr/sites/default/files/medias/documents/working-paper_384_2012.pdf
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Paper provided by Banque de France in its series Working papers with number 384.

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Length: 33 pages
Date of creation: 2012
Handle: RePEc:bfr:banfra:384
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Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS

Web page: http://www.banque-france.fr/

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  1. 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.
  2. Diebold & Rudebusch, "undated". "Measuring Business Cycle: A Modern Perspective," Home Pages _061, University of Pennsylvania.
  3. Banbura, Marta & Rünstler, Gerhard, 2007. "A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 0751, European Central Bank.
  4. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542, April.
  5. repec:hal:journl:peer-00844811 is not listed on IDEAS
  6. Chevillon, Guillaume & Hendry, David F., 2005. "Non-parametric direct multi-step estimation for forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 21(2), pages 201-218.
  7. Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," International Finance Discussion Papers 684, Board of Governors of the Federal Reserve System (U.S.).
  8. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00638009, HAL.
  9. Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2006. "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," The Warwick Economics Research Paper Series (TWERPS) 777, University of Warwick, Department of Economics.
  10. Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 0633, European Central Bank.
  11. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank, Research Centre.
  12. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
  13. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
  14. 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.
  15. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Forecasting with mixed-frequency data," University of Cyprus Working Papers in Economics 10-2010, University of Cyprus Department of Economics.
  16. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  17. Guérin, Pierre & Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
  18. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 521-536.
  19. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
  20. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
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