Testing exogeneity in nonparametric instrumental variables models identified by conditional quantile restrictions

Jia Young Michael Fu, Joel L. Horowitz, Matthias Parey

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a test for exogeneity of explanatory variables in a nonparametric instrumental variables (IV) model whose structural function is identified through a conditional quantile restriction. Quantile regression models are increasingly important in applied econometrics. As with mean-regression models, an erroneous assumption that the explanatory variables in a quantile regression model are exogenous can lead to highly misleading results. In addition, a test of exogeneity based on an incorrectly specified parametric model can produce misleading results. This paper presents a test of exogeneity that does not assume that the structural function belongs to a known finite-dimensional parametric family and does not require estimation of this function. The latter property is important because nonparametric estimates of the structural function are unavoidably imprecise. The test presented here is consistent whenever the structural function differs from the conditional quantile function on a set of nonzero probability. The test has nontrivial power uniformly over a large class of structural functions that differ from the conditional quantile function by $O({n^{ - 1/2}})$. The results of Monte Carlo experiments and an empirical application illustrate the performance of the test.

Original languageEnglish (US)
Pages (from-to)23-40
Number of pages18
JournalEconometrics Journal
Volume24
Issue number1
DOIs
StatePublished - Jan 1 2021

Keywords

  • hypothesis test
  • instrumental variables
  • quantile estimation
  • specification testing

ASJC Scopus subject areas

  • Economics and Econometrics

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