Forecast comparison of principal component regression and principal covariate regression

Abstract

Forecasting with many predictors is of interest, for instance, inmacroeconomics and finance. This paper compares two methods for dealing withmany predictors, that is, principal component regression (PCR) and principalcovariate regression (PCovR). Theforecast performance of these methods is compared by simulating data fromfactor 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.economic forecasting;principal components;factor model;principal covariates;regression model