Forecast combination through dimension reduction techniques

B-Tier
Journal: International Journal of Forecasting
Year: 2011
Volume: 27
Issue: 2
Pages: 224-237

Score contribution per author:

0.503 = (α=2.01 / 4 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

This paper considers several methods of producing a single forecast from several individual ones. We compare “standard” but hard to beat combination schemes (such as the average of forecasts at each period, or consensus forecast and OLS-based combination schemes) with more sophisticated alternatives that involve dimension reduction techniques. Specifically, we consider principal components, dynamic factor models, partial least squares and sliced inverse regression.

Technical Details

RePEc Handle
repec:eee:intfor:v:27:y:2011:i:2:p:224-237
Journal Field
Econometrics
Author Count
4
Added to Database
2026-01-29