Jean-Pierre Florens, Anna Simoni
arXiv 19 Oct 2021 · Econometrics · publishedAnnals of Economics and Statistics (2021) · 8 citations (OpenAlex)
arXiv:2110.09954 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the role played by identification in the Bayesian analysis of statistical and econometric models. First, for unidentified models we demonstrate that there are situations where the introduction of a non-degenerate prior distribution can make a parameter that is nonidentified in frequentist theory identified in Bayesian theory. In other situations, it is preferable to work with the unidentified model and construct a Markov Chain Monte Carlo (MCMC) algorithms for it instead of introducing identifying assumptions. Second, for partially identified models we demonstrate how to construct the prior and posterior distributions for the identified set parameter and how to conduct Bayesian analysis. Finally, for models that contain some parameters that are identified and others that are not we show that marginalizing out the identified parameter from the likelihood with respect to its conditional prior, given the nonidentified parameter, allows the data to be informative about the nonidentified and partially identified parameter. The paper provides examples and simulations that illustrate how to implement our techniques.
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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 | Chen, Christensen \ Tamer (2018) `Monte Carlo confidence sets for identified sets', Econometrica 86(6), 1965–2018 | 0.928 | 4 | 3 | 100% |
| 2 | Florens, Mouchart \ Rolin (1990) Elements of Bayesian statistics., Dekker - New York | 0.874 | 5 | 2 | 100% |
| 3 | Liao \ Jiang (2010) `Bayesian analysis in moment inequality models', Annals of Statistics 38, 275–316 | 0.737 | 3 | 2 | 100% |
| 4 | Liao \ Simoni (2019) `Bayesian inference for partially identified smooth convex models', Journal of Econometrics 211, 338–360 | 0.644 | 2 | 2 | 100% |
| 5 | Florens \ Mouchart (1986) `Exhaustivité, ancillante et identification en statistique Bayésienne', Annales d'Economie et Statistiques 4, 63–93 | 0.511 | 2 | 1 | 100% |
| 6 | Florens, Mouchart \ Rolin (1985) `On two definitions of identification', Statistics 16, 213–218 | 0.511 | 2 | 1 | 100% |
| 7 | Kadane (1974) The role of identification in Bayesian theory, in S. Fienberg \ A. Zellner, eds, `Studies in Bayesian Econometrics and Statistic… | 0.511 | 2 | 1 | 100% |
| 8 | Lindley (1971) Bayesian statistics: a review, Philadelphia, SIAM | 0.511 | 2 | 1 | 100% |
| 9 | Molchanov (2005) Theory of random sets, Springer | 0.511 | 2 | 1 | 100% |
| 10 | Poirier (1998) `Revising beliefs in nonidentified models', Econometric Theory 14(4), 483–509 | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nonparametric Bayesian Policy Learning | 0.511 | 2 | 1 |
| 2 | Finite Population Identification and Design-Based Sensitivity Analysis | 0.405 | 1 | 1 |