Name
Title | ||
|---|---|---|
Forecast comparison of principal component regression and principal covariate regression |
Abstract | ||
|---|---|---|
Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. The forecast accuracy of two methods for dealing with many predictors is compared, that is, principal component regression (PCR) and principal covariate regression (PCovR). Simulation experiments with data generated by factor models and regression models indicate that, in general, PCR performs better for the first type of data and PCovR performs better for the second type of data. An empirical application to four key US macroeconomic variables shows that PCovR achieves improved forecast accuracy in some situations. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.csda.2006.10.019 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
principal covariates,forecast comparison,factor model,empirical application,simulation experiment,forecast accuracy,macroeconomic variable,principal component regression,principal covariate regression,key us,principal components,regression model,economic forecasting | Cross-sectional regression,Econometrics,Covariate,Economics,Principal component regression,Regression diagnostic,Regression analysis,Local regression,Factor regression model,Statistics,Regression dilution | Journal |
Volume | Issue | ISSN |
51 | 7 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
8 | 1.04 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christiaan Heij | 1 | 52 | 8.06 |
| Patrick J. F. Groenen | 2 | 84 | 11.72 |
| Dick van Dijk | 3 | 70 | 7.09 |