Forecasting macroeconomic variables in data-rich environments

C-Tier
Journal: Economics Letters
Year: 2016
Volume: 138
Issue: C
Pages: 50-52

Authors (2)

Medeiros, Marcelo C. (University of Illinois at Urba...) Vasconcelos, Gabriel F.R. (not in RePEc)

Score contribution per author:

0.503 = (α=2.01 / 2 authors) × 0.5x C-tier

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

Abstract

We show that high-dimensional models produce, on average, smaller forecasting errors for macroeconomic variables when we consider a large set of predictors. Our results showed that a good selection of the adaptive LASSO hyperparameters also reduces forecast errors.

Technical Details

RePEc Handle
repec:eee:ecolet:v:138:y:2016:i:c:p:50-52
Journal Field
General
Author Count
2
Added to Database
2026-01-26