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 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.
Conditional heteroscedasticity; Local polynomial estimator; Nonparametric multivariate regression; Seemingly unrelated regressions; Vector autoregressions
Journal of Business and Economic Statistics
Taylor and Francis
SU, Liangjun and Ullah, A..
Testing Conditional Uncorrelatedness. (2009). Journal of Business and Economic Statistics. 27, (1), 18-29. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/354
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