Inference in models with adaptive learning

A-Tier
Journal: Journal of Monetary Economics
Year: 2010
Volume: 57
Issue: 3
Pages: 341-351

Score contribution per author:

1.341 = (α=2.01 / 3 authors) × 2.0x A-tier

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

Abstract

Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be conducted using the Anderson-Rubin statistic with appropriate choice of instruments. Application of this method to a typical new Keynesian sticky-price model with perpetual learning demonstrates its usefulness in practice.

Technical Details

RePEc Handle
repec:eee:moneco:v:57:y:2010:i:3:p:341-351
Journal Field
Macro
Author Count
3
Added to Database
2026-01-25