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Macroeconomic forecasting with matched principal components

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  • Heij, Christiaan
  • van Dijk, Dick
  • Groenen, Patrick J.F.

Abstract

This article proposes an improved method for the construction of principal components in macroeconomic forecasting. The underlying idea is to maximize the amount of variance of the original predictor variables that is retained by the components in order to reduce the variance involved in estimating the forecast model. This is achieved by matching the data window used for constructing the components with the estimation window. Extensive Monte Carlo simulations, using dynamic factor models, clarify the relationship between the achieved reduction in forecast variance and various design parameters, such as the observation length, the number of predictors, and the length of the forecast horizon. The method is also used in an empirical application to forecast eight key US macroeconomic time series over various horizons, where the components are constructed from a large set of predictors. The results show that the proposed modification leads, on average, to more accurate forecasts than previously used principal component regression methods.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 24 (2008)
Issue (Month): 1 ()
Pages: 87-100

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Handle: RePEc:eee:intfor:v:24:y:2008:i:1:p:87-100

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Web page: http://www.elsevier.com/locate/ijforecast

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References

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  1. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
  2. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
  3. Anindya BANERJEE & Massimiliano MARCELLINO, 2002. "Are There Any Reliable Leading Indicators for US Inflation and GDP Growth?," Economics Working Papers ECO2002/21, European University Institute.
  4. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  5. Serena Ng & Pierre Perron, 2005. "A Note on the Selection of Time Series Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(1), pages 115-134, 02.
  6. Valentina Corradi & Norman Swanson, 2004. "Predictive Density Evaluation," Departmental Working Papers 200419, Rutgers University, Department of Economics.
  7. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  8. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, 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. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
  11. Valentina Corradi & Norman Swanson, 2006. "Predictive Density Evaluation. Revised," Departmental Working Papers 200621, Rutgers University, Department of Economics.
  12. Bai, Jushan & Ng, Serena, 2006. "Evaluating latent and observed factors in macroeconomics and finance," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 507-537.
  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.
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Cited by:
  1. Björn Hagströmer & Richard G. Anderson & Jane M. Binner & Birger Nilsson, 2009. "Dynamics in systematic liquidity," Working Papers 2009-025, Federal Reserve Bank of St. Louis.
  2. Massimo Guidolin & Stuart Hyde, 2011. "Can VAR Models Capture Regime Shifts in Asset Returns? A Long-Horizon Strategic Asset Allocation Perspective," Working Papers 414, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  3. Guidolin, Massimo & Hyde, Stuart, 2012. "Simple VARs cannot approximate Markov switching asset allocation decisions: An out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3546-3566.

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