Double robust inference for continuous updating GMM

78 Pages Posted: 18 May 2021

See all articles by Frank R. Kleibergen

Frank R. Kleibergen

University of Amsterdam - Department of Quantitative Economics (KE)

Date Written: May 10, 2021

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. 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.

Keywords: Weak identification, misspecification, robust inference, Lagrange multiplier

JEL Classification: C10, C12, G10

Suggested Citation

Kleibergen, Frank R., Double robust inference for continuous updating GMM (May 10, 2021). Available at SSRN: https://ssrn.com/abstract=3847629 or http://dx.doi.org/10.2139/ssrn.3847629

Frank R. Kleibergen (Contact Author)

University of Amsterdam - Department of Quantitative Economics (KE) ( email )

Roetersstraat 11
Amsterdam, 1018 WB
Netherlands

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