Siddhartha Chib, Minchul Shin, Anna Simoni
arXiv 8 Mar 2026 · Econometrics · 1 citations (OpenAlex)
arXiv:2603.07780 · PDF · DOI · OpenAlex · Extracted main text
A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this paper, using the exponentially tilted empirical likelihood framework, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity against an extended model that is correctly specified in either case. We provide a comprehensive study of the log-marginal exponentially tilted empirical likelihood. We demonstrate that our testing procedure is consistent from a frequentist point of view: as the sample grows, it almost surely selects the base model if and only if the regressors are exogenous, and the extended model if and only if the regressors are endogenous. The methods are illustrated with simulated data, and problems concerning the causal effect of automobile prices on automobile demand and the causal effect of potentially endogenous airplane ticket prices on passenger volume.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Siddhartha Chib and Minchul Shin and Anna Simoni Bayesian estimation and comparison of moment condition models self | 0.941 | 18 | 4 | 83% |
| 2 | Siddhartha Chib Marginal likelihood from the Gibbs output self | 0.928 | 4 | 3 | 100% |
| 3 | Siddhartha Chib and Ivan Jeliazkov Marginal likelihood from the Metropolis-Hastings output self | 0.737 | 3 | 3 | 67% |
| 4 | Schennach, Susanne M Bayesian exponentially tilted empirical likelihood | 0.737 | 3 | 2 | 100% |
| 5 | Siddhartha Chib and Edward Greenberg Understanding the Metropolis-Hastings algorithm self | 0.644 | 2 | 2 | 100% |
| 6 | Andrews, Donald W. K Consistent Moment Selection Procedures for Generalized Method of Moments Estimation | 0.511 | 2 | 2 | 50% |
| 7 | Hong,Han and Preston,Bruce and Shum,Matthew Generalized Empirical Likelihood-Based Model Selection Criteria for Moment Condition Models | 0.511 | 2 | 2 | 50% |
| 8 | Sin, C.Y. and White, H Information Criteria for Selecting Possibly Misspecified Parametric Models | 0.511 | 2 | 1 | 100% |
| 9 | Donald W. K. Andrews and Biao Lu Consistent Model and Moment Selection Procedures for GMM Estimation with Application to Dynamic Panel Data Models | 0.405 | 1 | 1 | 100% |
| 10 | J.C. Chao and P.C.B. Phillips Posterior distributions in limited information analysis of the simultaneous equations model using the Jeffreys prior | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 36 scored citations.