Learning, Parameter Drift, and the Credibility Revolution

Hennessy, C and Livdan, D (2020) Learning, Parameter Drift, and the Credibility Revolution. Journal of Monetary Economics. ISSN 0304-3932 (In Press)

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.

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Item Type: Article
Subject Areas: Finance
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© 2020 Elsevier. This manuscript version is made available under the Creative Commons CC-BY-NC-ND 4.0 license

Subjects: L > Learning
Date Deposited: 10 Feb 2020 10:10
Last Modified: 24 Feb 2020 10:38
URI: https://lbsresearch.london.edu/id/eprint/1365
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