Testing Conditional Uncorrelatedness

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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Bibliographic Details
Main Authors: SU, Liangjun, ULLAH, Aman
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2009
Subjects:
Conditional heteroscedasticity
Local polynomial estimator
Nonparametric multivariate regression
Seemingly unrelated regressions
Vector autoregressions
Econometrics
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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Institution: Singapore Management University
Language: English
Description
Summary: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.

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