Learning, parameter drift, and the credibility revolution

Christopher A. Hennessy and Dmitry Livdan

Journal of Monetary Economics, 2021, vol. 117, issue C, 395-417

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.

Keywords: Natural experiment; Causality; Uncertainty; Learning (search for similar items in EconPapers)
JEL-codes: E62 E63 G18 G28 G38 H00 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:moneco:v:117:y:2021:i:c:p:395-417

DOI: 10.1016/j.jmoneco.2020.02.003

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