Forecast comparison of principal component regression and principal covariate regression

C. Heij, Patrick Groenen () and D.J.C. van Dijk
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D.J.C. van Dijk: Erasmus Econometric Institute

No EI 2005-28 Revision_Date: 2009-10-28, Econometric Institute Report from Erasmus University Rotterdam, Econometric Institute

Abstract: Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. This paper compares two methods for dealing with many predictors, that is, principal component regression (PCR) and principal covariate regression (PCovR). The forecast performance of these methods is compared by simulating data from factor models and from regression models. The simulations show that, in general, PCR performs better for the first type of data and PCovR performs better for the second type of data. The simulations also clarify the effect of the choice of the PCovR weight on the orecast quality.

Keywords: principal components; principal covariates; regression model; factor model; economic forecasting (search for similar items in EconPapers)
Date: 2005-08-02

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Related works:
Journal Article: Forecast comparison of principal component regression and principal covariate regression (2007)
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Persistent link: http://EconPapers.repec.org/RePEc:dgr:eureir:1765006918

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