Conditional inference with a functional nuisance parameter

• Autores: Isaiah Andrews, Anna Mikusheva
• Localización: Econométrica: Journal of the Econometric Society, ISSN 0012-9682, Vol. 84, Nº 4, 2016, págs. 1571-1612
• Idioma: inglés
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• Resumen

  • This paper shows that the problem of testing hypotheses in moment condition models without any assumptions about identification may be considered as a problem of testing with an infinite-dimensional nuisance parameter. We introduce a sufficient statistic for this nuisance parameter in a Gaussian problem and propose conditional tests. These conditional tests have uniformly correct asymptotic size for a large class of models and test statistics. We apply our approach to construct tests based on quasi-likelihood ratio statistics, which we show are efficient in strongly identified models and perform well relative to existing alternatives in two examples.