Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression

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 conv...

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Main Authors: SU, Liangjun, WHITE, Halbert L.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/soe_research/1433
https://ink.library.smu.edu.sg/context/soe_research/article/2432/viewcontent/hw_11_12_11_qr_ci_test.pdf
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spelling sg-smu-ink.soe_research-24322014-02-25T08:55:21Z Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression SU, Liangjun WHITE, Halbert L. 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. 2012-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1433 info:doi/10.1108/S0731-9053(2012)0000029018 https://ink.library.smu.edu.sg/context/soe_research/article/2432/viewcontent/hw_11_12_11_qr_ci_test.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Conditional independence Empirical process Granger causality Local polynomial Quantile regression Specification test Uniform local Bahadur representation Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Conditional independence
Empirical process
Granger causality
Local polynomial
Quantile regression
Specification test
Uniform local Bahadur representation
Econometrics
spellingShingle Conditional independence
Empirical process
Granger causality
Local polynomial
Quantile regression
Specification test
Uniform local Bahadur representation
Econometrics
SU, Liangjun
WHITE, Halbert L.
Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression
description 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.
format text
author SU, Liangjun
WHITE, Halbert L.
author_facet SU, Liangjun
WHITE, Halbert L.
author_sort SU, Liangjun
title Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression
title_short Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression
title_full Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression
title_fullStr Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression
title_full_unstemmed Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression
title_sort conditional independence specification testing for dependent processes with local polynomial quantile regression
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/soe_research/1433
https://ink.library.smu.edu.sg/context/soe_research/article/2432/viewcontent/hw_11_12_11_qr_ci_test.pdf
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