Structural Estimation Under Misspecification: Theory and Implications for Practice*

• 成果类型: Article
• 署名作者: Andrews, Isaiah; Barahona, Nano; Gentzkow, Matthew; Rambachan, Ashesh; Shapiro, Jesse M.
• 署名单位: Massachusetts Institute of Technology (MIT); National Bureau of Economic Research; University of California System; University of California Berkeley; Stanford University; Harvard University
• 刊物名称: QUARTERLY JOURNAL OF ECONOMICS
• ISSN/ISSBN: 0033-5533
• DOI: 10.1093/qje/qjaf018
• 发表日期: 2025
• 页码: 1801-1855
• 关键词: discrete-choice models; differentiated products; generalized-method; market power; identification; EFFICIENCY; demand; functionals; sensitivity; EQUATIONS

摘要

A researcher can use a tightly parameterized structural model to obtain internally consistent estimates of a wide range of economically interesting targets. We ask how reliable these estimates are when the researcher's model may be misspecified. We focus on the case of multivariate, potentially nonlinear models where the causal variable of interest is endogenous. Reliable estimates require that the researcher's model is flexible enough to describe the effects of the endogenous variable approximately correctly. Reliable estimates do not require that the researcher has correctly specified the role of the exogenous controls in the model. However, if the role of the controls is misspecified, reliable estimates require a property we call strong exclusion. Strong exclusion depends on having sufficiently many instruments that are unrelated to the controls. We discuss how practitioners can achieve strong exclusion, and illustrate our findings with an application to a differentiated goods model of demand for beer.