Identifying assumptions and research dynamics

Speaker
Ran Spiegler
Date
05/03/2024 - 12:15 - 11:00Add To Calendar 2024-03-05 11:00:00 2024-03-05 12:15:00 Identifying assumptions and research dynamics A representative researcher pursuing a question has repeated opportunities for empirical research. To process findings, she must impose an “identifying assumption”, which ensures that repeated observation would provide a definitive answer to her question. Research designs vary in quality and are implemented only when the assumption is plausible enough according to a KL-divergence-based criterion, and then beliefs are Bayes-updated as if the assumption were perfectly valid. We study the dynamics of this learning process and its induced long-run beliefs. The rate of research cannot uniformly accelerate over time. We characterize environments in which it is stationary. Long-run beliefs can exhibit history-dependence. We apply the model to stylized examples of empirical methodologies: experiments, causal-inference techniques, and (in an extension) “structural” identification methods such as “calibration” and “Heckman selection.” Joint with Andrew Ellis BIU Economics common room אוניברסיטת בר-אילן - Department of Economics Economics.Dept@mail.biu.ac.il Asia/Jerusalem public
Place
BIU Economics common room
Affiliation
Tel Aviv University & University College London
Abstract

A representative researcher pursuing a question has repeated opportunities for empirical research. To process findings, she must impose an “identifying assumption”, which ensures that repeated observation would provide a definitive answer to her question. Research designs vary in quality and are implemented only when the assumption is plausible enough according to a KL-divergence-based criterion, and then beliefs are Bayes-updated as if the assumption were perfectly valid. We study the dynamics of this learning process and its induced long-run beliefs. The rate of research cannot uniformly accelerate over time. We characterize environments in which it is stationary. Long-run beliefs can exhibit history-dependence. We apply the model to stylized examples of empirical methodologies: experiments, causal-inference techniques, and (in an extension) “structural” identification methods such as “calibration” and “Heckman selection.”

Joint with Andrew Ellis

Last Updated Date : 25/02/2024