Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment Models

Econometrica · 2015 · Volume 83 · Issue 3

Abstract

This paper considers inference on functionals of semi/nonparametric conditional moment restrictions with possibly nonsmooth generalized residuals, which include all of the (nonlinear) nonparametric instrumental variables (IV) as special cases. These models are often ill-posed and hence it is difficult to verify whether a (possibly nonlinear) functional is root-nestimable or not. We provide computationally simple, unified inference procedures that are asymptotically valid regardless of whether a functional is root-nestimable or not. We establish the following new useful results: (1) the asymptotic normality of a plug-in penalized sieve minimum distance (PSMD) estimator of a (possibly nonlinear) functional; (2) the consistency of simple sieve variance estimators for the plug-in PSMD estimator, and hence the asymptotic chi-square distribution of the sieve Wald statistic; (3) the asymptotic chi-square distribution of an optimally weighted sieve quasi likelihood ratio (QLR) test under the null hypothesis; (4) the asymptotic tight distribution of a non-optimally weighted sieve QLR statistic under the null; (5) the consistency of generalized residual bootstrap sieve Wald and QLR tests; (6) local power properties of sieve Wald and QLR tests and of their bootstrap versions; (7) asymptotic properties of sieve Wald and SQLR for functionals of increasing dimension. Simulation studies and an empirical illustration of a nonparametric quantile IV regression are presented.

Keywords

Nonlinear nonparametric instrumental variables, penalized sieve minimum distance, irregular functional, sieve variance estimators, sieve Wald, sieve quasi likelihood ratio, generalized residual bootstrap, local power, Wilks phenomenon

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