Publication Type
Working Paper
Version
publishedVersion
Publication Date
1-2013
Abstract
We construct two classes of smoothed empirical likelihood ratio tests for the conditional independence hypothesis by writing the null hypothesis as an infinite collection of conditional moment restrictions indexed by a nuisance parameter. One class is based on the CDF; another is based on smoother functions. We show that the test statistics are asymptotically normal under the null hypothesis and a sequence of Pitman local alternatives. We also show that the tests possess an asymptotic optimality property in terms of average power. Simulations suggest that the tests are well behaved in finite samples. Applications to some economic and financial time series indicate that our tests reveal some interesting nonlinear causal relations which the traditional linear Granger causality test fails to detect.
Keywords
Conditional independence, Empirical likelihood, Granger causality, Local bootstrap, Nonlinear dependence, Nonparametric regression, U-statistics
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
34
Citation
SU, Liangjun and WHITE, Halbert.
Testing Conditional Independence via Empirical Likelihood. (2013). 1-34.
Available at: https://ink.library.smu.edu.sg/soe_research/1477
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Journal of Econometrics, 2014, 182, 27-44. http://dx.doi.org/10.1016/j.jeconom.2014.04.006