Doubly Robust GMM Inference and Differentiated Products Demand Models

Stéphane Auray, Nicolas Lepage-Saucier () and Purevdorj Tuvaandor ()
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Nicolas Lepage-Saucier: CREST; ENSAI
Purevdorj Tuvaandor: CREST; ENSAI

No 2018-13, Working Papers from Center for Research in Economics and Statistics

Abstract: This paper develops robust inference methods for moment condition models implemented with a n1=2-consistent auxiliary estimator of the nuisance parameters. When applied to models subject to weak identification and boundary parameter problems; they simultaneously overcome both irregularities and are asymptotically pivotal with minimal assumptions on the parameter space. If these problems are not present in the data; they are asymptotically equivalent to standard statistics for nonlinear models. They also have similar computational requirements. We apply our tests to the differentiated products demand model; which may suffer from both problems: the variance of the random coefecients is often close to zero; causing the boundary parameter problem; and the strength of the available instruments is often put in doubt; which may cause weak identification. We evaluate the performance of the proposed tests by simulations.

Keywords: Boundary parameter; heterogeneity; pivotal statistic; random utility; robust inference; weak identification. (search for similar items in EconPapers)
Pages: 78 pages
Date: 2018-08-25
New Economics Papers: this item is included in nep-ecm and nep-upt
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