Uncertain identification

• 成果类型: Article
• 署名作者: Giacomini, Raffaella; Kitagawa, Toru; Volpicella, Alessio
• 署名单位: University of London; University College London; Federal Reserve System - USA; Federal Reserve Bank - Chicago; Kyoto University; University of Surrey
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1671
• 发表日期: 2022
• 页码: 95-123
• 关键词: partial identification; sensitivity analysis; model averaging; Bayesian robustness; ambiguity; C11; C32; C52

摘要

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point- and set-identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set-identified models and combines them with a single posterior for models that are either point-identified or that impose nondogmatic assumptions. The output is a set of posteriors (post-averaging ambiguous belief), which can be summarized by reporting the set of posterior means and the associated credible region. We clarify when the prior model probabilities are updated and characterize the asymptotic behavior of the posterior model probabilities. The method provides a formal framework for conducting sensitivity analysis of empirical findings to the choice of identifying assumptions. For example, we find that in a standard monetary model one would need to attach a prior probability greater than 0.28 to the validity of the assumption that prices do not react contemporaneously to a monetary policy shock, in order to obtain a negative response of output to the shock.