Bayesian Inference in IV Regressions

Domenico Giannone, Michele Lenza and Giorgio Primiceri

No 34648, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C01 C11 C12 C26 (search for similar items in EconPapers)
Date: 2026-01
Note: LS TWP
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