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Macroeconomic forecasting with matched principal components

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

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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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
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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  1. 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.
  2. 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.
  3. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  4. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
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  7. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  8. Valentina Corradi & Norman Swanson, 2004. "Predictive Density Evaluation," Departmental Working Papers 200419, Rutgers University, Department of Economics.
  9. Serena Ng & Pierre Perron, 2001. "A Note on the Selection of Time Series Models," Boston College Working Papers in Economics 500, Boston College Department of Economics.
  10. 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.
  11. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
  12. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
  13. Valentina Corradi & Norman Swanson, 2006. "Predictive Density Evaluation. Revised," Departmental Working Papers 200621, Rutgers University, Department of Economics.
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