CAREER: Robust Econometric Inference
This project develops new econometric and statistical tools for drawing inferences about economic relationships in contexts where existing techniques are unreliable. In many economic contexts, the available data contain relatively little usable information about the relationships of economic interest, or are poorly described by available econometric models. In such cases, existing techniques for inference can be unreliable, and researchers using these techniques risk drawing spurious conclusions about relationships among economic variables and the likely effects of proposed policies. This project develops new techniques for reliable inference in such settings, allowing researchers to more accurately quantify uncertainty about economic relationships and policy proposals. To increase the impact of this research, the investigator organizes, records, and publicly posts a series of lectures discussing the proposed methods, and also posts code for the methods developed to publicly accessible software repositories. Further, the investigator participates in a mentoring program for graduate students from underrepresented minority backgrounds organized by the American Economic Association.
The project consists of four components. The first component uses tools from differential geometry to study weak identification-robust inference in instrumental variables models with heterogeneous treatment effects, closing an important gap in the existing literature on identification-robust inference. The second component derives optimal tests and estimators for moment equality models with potential identification failure, using a novel family of integrated likelihoods which integrate out a prior on a functional nuisance parameter. The third component shows that by exploiting structure common to many partially-identified moment inequality applications one can obtain powerful, computationally simple inference for low-dimensional parameters of interest. The fourth and final component considers locally misspecified models, develops a connection between inference under local misspecification and inference under weak identification, and proposes techniques which allow misspecification-robust inference on structural or causal parameters of interest.
Supported by the National Science Foundation grant #1654234
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