Learning, parameter drift, and the credibility revolution

A-Tier
Journal: Journal of Monetary Economics
Year: 2021
Volume: 117
Issue: C
Pages: 395-417

Authors (2)

Hennessy, Christopher A. (not in RePEc) Livdan, Dmitry (University of California-Berke...)

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

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

Abstract

This paper analyses extrapolation and inference using tax experiments in dynamic economies when shock processes are latent regime-shifting Markov chains. Belief revisions result in severe parameter drift: Response signs and magnitudes vary widely over time despite ideal exogeneity. Even with linear causal effects, shock responses are non-linear, preventing direct extrapolation. Analytical formulae are derived for extrapolating responses or inferring causal parameters. Extrapolation and inference hinges upon shock histories and correct assumptions regarding potential data generating processes. A martingale condition is necessary and sufficient for shock responses to directly recover comparative statics, but stochastic monotonicity is insufficient for correct sign inference.

Technical Details

RePEc Handle
repec:eee:moneco:v:117:y:2021:i:c:p:395-417
Journal Field
Macro
Author Count
2
Added to Database
2026-01-25