Bayesian forecasting with highly correlated predictors

C-Tier
Journal: Economics Letters
Year: 2013
Volume: 118
Issue: 1
Pages: 148-150

Score contribution per author:

1.005 = (α=2.01 / 1 authors) × 0.5x C-tier

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

Abstract

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

Technical Details

RePEc Handle
repec:eee:ecolet:v:118:y:2013:i:1:p:148-150
Journal Field
General
Author Count
1
Added to Database
2026-01-25