arXiv 21 Nov 2022 · Econometrics
arXiv:2211.11915 · PDF · DOI · OpenAlex · Extracted main text
Empirical researchers often perform model specification tests, such as Hausman tests and overidentifying restrictions tests, to assess the validity of estimators rather than that of models. This paper examines the effectiveness of such specification pretests in detecting invalid estimators. We analyze the local asymptotic properties of test statistics and estimators and show that locally unbiased specification tests cannot determine whether asymptotically efficient estimators are asymptotically biased. In particular, an estimator may remain valid even when the null hypothesis of correct model specification is false, and it may be invalid even when the null hypothesis is true. The main message of the paper is that correct model specification and valid estimation are distinct issues: correct specification is neither necessary nor sufficient for asymptotically unbiased estimation.
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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, X. and A. Santos (2018) Overidentification in regular models | 1.000 | 8 | 4 | 100% |
| 2 | Newey, W. K (1985) Generalized method of moments specification testing | 1.000 | 5 | 3 | 100% |
| 3 | Hall, A. R (2005) Generalized Method of Moments | 0.874 | 6 | 2 | 100% |
| 4 | Andrews, I., J. Chen, and O. Tecchio (2025) The purpose of an estimator is what it does: Misspecification, estimands, and over-identification | 0.737 | 3 | 2 | 100% |
| 5 | van der Vaart, A. W (1998) Asymptotic Statistics | 0.737 | 3 | 2 | 100% |
| 6 | Sueishi, N (2024) Large sample justifications for the bayesian empirical likelihood self | 0.644 | 3 | 2 | 67% |
| 7 | Armstrong, T. B. and M. Kolesár (2021) Sensitivity analysis using approximate moment condition models | 0.644 | 2 | 2 | 100% |
| 8 | Kitamura, Y., T. Otsu, and K. Evdokimov (2013) Robustness, infinitesimal neighborhoods, and moment restrictions | 0.644 | 2 | 2 | 100% |
| 9 | Komunjer, I. and G. Ragusa (2016) Existence and characterization of conditional density projections | 0.511 | 2 | 2 | 50% |
| 10 | Guggenberger, P. and G. Kumar (2012) On the size distortion of tests after an overidentifying restrictions pretest | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 33 scored citations.