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A medium-N approach to macroeconomic forecasting

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  • Cubadda, Gianluca
  • Guardabascio, Barbara

Abstract

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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Bibliographic Info

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

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Web page: http://www.elsevier.com/locate/inca/30411

Related research

Keywords: Partial least squares; Principal component regression; Dynamic factor models; Data-rich forecasting methods; Dimension-reduction techniques;

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  1. 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.
  2. Centoni, Marco & Cubadda, Gianluca & Hecq, Alain, 2003. "Common Shocks, Common Dynamics, and the International Business Cycle," Economics & Statistics Discussion Papers esdp03007, University of Molise, Dept. EGSeI.
  3. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
  4. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  5. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," CEPR Discussion Papers 3432, C.E.P.R. Discussion Papers.
  6. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.
  7. 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.
  8. 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.
  9. 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.
  10. Gianluca Cubadda & Alain Hecq, 2011. "Testing for common autocorrelation in data‐rich environments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 325-335, April.
  11. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  12. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
  13. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  14. 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.
  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. 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.
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Cited by:
  1. Gianluca Cubadda & Barbara Guardabascio & Alain Hecq, 2012. "A General to Specific Approach for Constructing Composite Business Cycle Indicators," CEIS Research Paper 224, Tor Vergata University, CEIS, revised 27 Feb 2012.

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