Forecast comparison of principal component regression and principal covariate regression
作者： Christiaan Heij , Patrick J.F. Groenen , Dick van Dijk
DOI: 10.1016/J.CSDA.2006.10.019
关键词: Principal (computer security) 、 Cross-sectional regression 、 Regression 、 Statistics 、 Covariate 、 Principal component analysis 、 Econometrics 、 Mathematics 、 Factor analysis 、 Principal component regression 、 Regression analysis
摘要: Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. The forecast accuracy two methods dealing compared, that is, principal component regression (PCR) covariate (PCovR). Simulation experiments data generated by factor models indicate that, general, PCR performs better the first type PCovR second data. An empirical application to four key US macroeconomic variables shows achieves improved some situations.
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