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Double robust inference for continuous updating GMM

Frank Kleibergen, Zhaoguo Zhan

arXiv 18 May 2021 · Econometrics · publishedQuantitative Economics (2025) · 4 citations (OpenAlex)

arXiv:2105.08345 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the pseudo-true value of the structural parameters in the generalized method of moments. The pseudo-true value is defined as the minimizer of the population continuous updating objective function and equals the true value of the structural parameter in the absence of misspecification.\nocite{hhy96} The (bounding) chi-squared limiting distribution of the DRLM statistic is robust to both misspecification and weak identification of the structural parameters, hence its name. To emphasize its importance for applied work, we use the DRLM test to analyze the return on education, which is often perceived to be weakly identified, using data from Card (1995) where misspecification occurs in case of treatment heterogeneity; and to analyze the risk premia associated with risk factors proposed in Adrian et al. (2014) and He et al. (2017), where both misspecification and weak identification need to be addressed.

Citation extraction

53
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101
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distinct cited
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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
1Kleibergen, F (2005) Testing Parameters in GMM without assuming that they are identified self1.00053100%
2Hansen, L.P., J. Heaton and A. Yaron (1996) Finite Sample Properties of Some Alternative GMM Estimators0.92844100%
3Stock, J.H. and J.H. Wright (2000) GMM with Weak Identification0.92844100%
4Andrews, I (2016) Conditional Linear Combination Tests for Weakly Identified Models0.92843100%
5Gospodinov, N., R. Kan and C. Robotti (2017) Spurious inference in Reduced-Rank Regresson Models0.92843100%
6Kleibergen, F (2009) Tests of Risk Premia in Linear Factor Models self0.92843100%
7Andrews, I. and A. Mikusheva (2016) Conditional inference with a functional nuisance parameter0.84333100%
8Kleibergen, F. and R. Paap (2006) Generalized Reduced Rank Tests using the Singular Value Decomposition self0.84333100%
9Kleibergen, F. and Z. Zhan (2020) Robust Inference for Consumption-Based Asset Pricing self0.84333100%
10Moreira, M.J (2003) A Conditional Likelihood Ratio Test for Structural Models0.84333100%

Showing the top 10 of 53 scored citations.

Cited by, within the corpus

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

Citing paperIntensityMentionsSections
1Sensitivity, Informativeness, and Misspecification in GMM Estimation0.51122
2Misspecification and Weak Identification in Asset Pricing0.40511
3Inference with Many Weak Instruments and Heterogeneity0.40511
4Plausible GMM: A Quasi-Bayesian Approach0.40511
5The purpose of an estimator is what it does: Misspecification, estimands, and over-identification0.40511
6True and Pseudo-True Parameters0.40511
7Pivotal and identification-robust nonparametric inference in linear IV models0.40511