Publication Type

Book Chapter

Version

acceptedVersion

Publication Date

1-2012

Abstract

We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also show that, despite our nonparametric approach, our tests can detect local alternatives to conditional independence that decay to zero at the parametric rate. Our approach gives the first nonparametric tests for time-series conditional independence that can detect local alternatives at the parametric rate. Monte Carlo simulations suggest that our tests perform well in finite samples. Our tests have a variety of uses in applications, such as testing conditional exogeneity or Granger non-causality.

Keywords

Conditional independence, Empirical process, Granger causality, Local polynomial, Quantile regression, Specification test, Uniform local Bahadur representation

Discipline

Econometrics

Research Areas

Econometrics

Publication

Essays in Honor of Jerry Hausman

Volume

29

Editor

Advances in Econometrics

First Page

355

Last Page

434

ISBN

9781781903087

Identifier

10.1108/S0731-9053(2012)0000029018

Publisher

Emerald

City or Country

Bingley

Additional URL

https://doi.org/10.1108/S0731-9053(2012)0000029018

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