Robust Inference in Models Identified via Heteroskedasticity

A-Tier
Journal: Review of Economics and Statistics
Year: 2022
Volume: 104
Issue: 3
Pages: 510-524

Score contribution per author:

4.022 = (α=2.01 / 1 authors) × 2.0x A-tier

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

Abstract

Identification via heteroskedasticity exploits variance changes between regimes to identify parameters in simultaneous equations. Weak identification occurs when shock variances change very little or multiple variances change close to proportionally, making standard inference unreliable. I propose an

Technical Details

RePEc Handle
repec:tpr:restat:v:104:y:2022:i:3:p:510-524
Journal Field
General
Author Count
1
Added to Database
2026-01-25