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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.

Suggested Citation

  • Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2008. "Macroeconomic forecasting with matched principal components," International Journal of Forecasting, Elsevier, vol. 24(1), pages 87-100.
  • Handle: RePEc:eee:intfor:v:24:y:2008:i:1:p:87-100
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    Cited by:

    1. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    2. 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.
    3. Guidolin, Massimo & Hyde, Stuart, 2012. "Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 695-716.
    4. 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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