Learning, parameter drift, and the credibility revolution

• 成果类型: Article
• 署名作者: Hennessy, Christopher A.; Livdan, Dmitry
• 署名单位: University of London; London Business School; University of California System; University of California Berkeley; Center for Economic & Policy Research (CEPR)
• 刊物名称: JOURNAL OF MONETARY ECONOMICS
• ISSN/ISSBN: 0304-3932
• 发表日期: 2021
• 关键词: natural experiment; causality; uncertainty; learning

摘要

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. (C) 2020 Published by Elsevier B.V.