Testing Conditional Uncorrelatedness

We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to t...

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Main Authors: SU, Liangjun, ULLAH, Aman
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Language:English
Published: Institutional Knowledge at Singapore Management University 2009
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Online Access:https://ink.library.smu.edu.sg/soe_research/354
https://ink.library.smu.edu.sg/context/soe_research/article/1353/viewcontent/testing_conditional_uncorrelatedness_afv.pdf
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spelling sg-smu-ink.soe_research-13532018-09-03T07:40:24Z Testing Conditional Uncorrelatedness SU, Liangjun ULLAH, Aman We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to the standard normal distribution asymptotically. We also study the local power property of the test. Simulation shows that the test behaves quite well in finite samples. 2009-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/354 info:doi/10.1198/jbes.2009.0002 https://ink.library.smu.edu.sg/context/soe_research/article/1353/viewcontent/testing_conditional_uncorrelatedness_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Conditional heteroscedasticity Local polynomial estimator Nonparametric multivariate regression Seemingly unrelated regressions Vector autoregressions Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Conditional heteroscedasticity
Local polynomial estimator
Nonparametric multivariate regression
Seemingly unrelated regressions
Vector autoregressions
Econometrics
spellingShingle Conditional heteroscedasticity
Local polynomial estimator
Nonparametric multivariate regression
Seemingly unrelated regressions
Vector autoregressions
Econometrics
SU, Liangjun
ULLAH, Aman
Testing Conditional Uncorrelatedness
description We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to the standard normal distribution asymptotically. We also study the local power property of the test. Simulation shows that the test behaves quite well in finite samples.
format text
author SU, Liangjun
ULLAH, Aman
author_facet SU, Liangjun
ULLAH, Aman
author_sort SU, Liangjun
title Testing Conditional Uncorrelatedness
title_short Testing Conditional Uncorrelatedness
title_full Testing Conditional Uncorrelatedness
title_fullStr Testing Conditional Uncorrelatedness
title_full_unstemmed Testing Conditional Uncorrelatedness
title_sort testing conditional uncorrelatedness
publisher Institutional Knowledge at Singapore Management University
publishDate 2009
url https://ink.library.smu.edu.sg/soe_research/354
https://ink.library.smu.edu.sg/context/soe_research/article/1353/viewcontent/testing_conditional_uncorrelatedness_afv.pdf
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