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True and Pseudo-True Parameters

Isaiah Andrews, Harvey Barnhard, Jacob Carlson

arXiv 16 Apr 2026 · Econometrics

arXiv:2604.15563 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Parameter estimates in misspecified models converge to pseudo-true parameter values, which minimize a population objective function. Pseudo-true values often differ from quantities of economic interest, raising questions of how, if at all, they are relevant for decision-making. To study this question we consider Bayesian decision-makers facing a linear population minimum distance problem. Within a class of priors motivated by the minimum distance objective, we characterize prior sequences under which posteriors concentrate on the pseudo-true value. This convergence is fragile to small changes in priors, implying that pseudo-true values are relevant for decision-making only in special cases. Constructive results are nevertheless possible in this setting, and we derive simple confidence intervals that guarantee correct average coverage for the true parameter under every prior in the class we study, with no bound on the magnitude of misspecification.

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29
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45
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
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10Alquier, Pierre and Ridgway, James and Chopin, Nicolas (2016) On the properties of variational approximations of Gibbs posteriors0.40511100%

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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Plausible GMM: A Quasi-Bayesian Approach0.73732
2Network-Adjusted GMM Estimation under Network Uncertainty0.40511