Testing Conditional Independence via Empirical Likelihood
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 f...
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sg-smu-ink.soe_research-24762015-12-10T07:05:31Z Testing Conditional Independence via Empirical Likelihood SU, Liangjun WHITE, Halbert 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. 2013-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1477 https://ink.library.smu.edu.sg/context/soe_research/article/2476/viewcontent/hw_2009_29_11_20testing_20conditional_20independence_20via_20empirical_20likelihood.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 likelihood Granger causality Local bootstrap Nonlinear dependence Nonparametric regression U-statistics Econometrics |
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Conditional independence Empirical likelihood Granger causality Local bootstrap Nonlinear dependence Nonparametric regression U-statistics Econometrics SU, Liangjun WHITE, Halbert Testing Conditional Independence via Empirical Likelihood |
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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. |
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SU, Liangjun WHITE, Halbert |
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SU, Liangjun WHITE, Halbert |
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SU, Liangjun |
title |
Testing Conditional Independence via Empirical Likelihood |
title_short |
Testing Conditional Independence via Empirical Likelihood |
title_full |
Testing Conditional Independence via Empirical Likelihood |
title_fullStr |
Testing Conditional Independence via Empirical Likelihood |
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Testing Conditional Independence via Empirical Likelihood |
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testing conditional independence via empirical likelihood |
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Institutional Knowledge at Singapore Management University |
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2013 |
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https://ink.library.smu.edu.sg/soe_research/1477 https://ink.library.smu.edu.sg/context/soe_research/article/2476/viewcontent/hw_2009_29_11_20testing_20conditional_20independence_20via_20empirical_20likelihood.pdf |
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