Salle 3001

Domenico GIANNONE (Johns Hopkins University) “Bayesian Inference in IV Regression”

Abstract: It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue arises because flat priors on the first-stage coefficients overstate instrument strength. In contrast, inference improves drastically when an uninformative prior is specified directly on the concentration parameter—the key nuisance parameter capturing instrument relevance. The  resulting Bayesian credible intervals are asymptotically equivalent to the frequentist confidence intervals based on conditioning approaches, and remain robust to weak instruments.

Joint work : Michele Lenza and Giorgio Primiceri

Organizer :  Alessandro RIBONI