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A medium-N approach to macroeconomic forecasting

  • Cubadda, Gianluca
  • Guardabascio, Barbara

This paper considers methods for forecasting macroeconomic time series in a framework where the number of predictors, N, is too large to apply traditional regression models but not sufficiently large to resort to statistical inference based on double asymptotics. Our interest is motivated by a body of empirical research suggesting that popular data-rich prediction methods perform best when N ranges from 20 to 40. In order to accomplish our goal, we resort to partial least squares and principal component regression to consistently estimate a stable dynamic regression model with many predictors as only the number of observations, T, diverges. We show both by simulations and empirical applications that the considered methods, especially partial least squares, compare well to models that are widely used in macroeconomic forecasting.

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File URL: http://www.sciencedirect.com/science/article/pii/S0264999312000910
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Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 29 (2012)
Issue (Month): 4 ()
Pages: 1099-1105

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Handle: RePEc:eee:ecmode:v:29:y:2012:i:4:p:1099-1105
DOI: 10.1016/j.econmod.2012.03.027
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/30411

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  1. Domenico Giannone & Martha Banbura & Lucrezia Reichlin, 2008. "Bayesian VARs with large panels," ULB Institutional Repository 2013/13388, ULB -- Universite Libre de Bruxelles.
  2. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2005. "The generalised dynamic factor model: one sided estimation and forecasting," ULB Institutional Repository 2013/10129, ULB -- Universite Libre de Bruxelles.
  3. Cubadda, Gianluca, 2007. "A unifying framework for analysing common cyclical features in cointegrated time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 896-906, October.
  4. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April.
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  6. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
  7. Gianluca Cubadda & Alain Hecq, 2009. "Testing for Common Autocorrelation in Data Rich Environments," CEIS Research Paper 153, Tor Vergata University, CEIS, revised 04 Dec 2009.
  8. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  9. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  10. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  11. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
  12. Almoy, Trygve, 1996. "A simulation study on comparison of prediction methods when only a few components are relevant," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 87-107, January.
  13. Centoni, Marco & Cubadda, Gianluca & Hecq, Alain, 2007. "Common shocks, common dynamics, and the international business cycle," Economic Modelling, Elsevier, vol. 24(1), pages 149-166, January.
  14. Groen, Jan J. J. & Kapetanios, George, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York, revised 01 Oct 2015.
  15. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  16. Gianluca Cubadda & Alain Hecq & Franz C. Palm, 2008. "Studying Co-Movements in Large Multivariate Data Prior to Multivariate Modelling," CEIS Research Paper 125, Tor Vergata University, CEIS, revised 14 Jul 2008.
  17. Hyonho Chun & Sündüz Keleş, 2010. "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 3-25.
  18. Caggiano, Giovanni & Kapetanios, George & Labhard, Vincent, 2009. "Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK," Working Paper Series 1051, European Central Bank.
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