Learning, Parameter Drift, and the Credibility Revolution

Hennessy, C and Livdan, D (2021) Learning, Parameter Drift, and the Credibility Revolution. Journal of Monetary Economics, 117. pp. 395-417. ISSN 0304-3932

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.