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Testing Conditional Uncorrelatedness

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  • Su, Liangjun
  • Ullah, Aman

Abstract

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.

Suggested Citation

  • Su, Liangjun & Ullah, Aman, 2009. "Testing Conditional Uncorrelatedness," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 18-29.
    • Handle: RePEc:bes:jnlbes:v:27:y:2009:p:18-29
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    Cited by:

    1. Lee, Jungyoon & Robinson, Peter, 2015. "Panel nonparametric regression with fixed effects," LSE Research Online Documents on Economics 61431, London School of Economics and Political Science, LSE Library.
    2. Guangming Pan & Jiti Gao & Yanrong Yang & Meihui Guo, 2015. "Cross-sectional Independence Test for a Class of Parametric Panel Data Models," Monash Econometrics and Business Statistics Working Papers 17/15, Monash University, Department of Econometrics and Business Statistics.
    3. Lee, Jungyoon & Robinson, Peter M., 2015. "Panel nonparametric regression with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 346-362.
    4. Xiangdong Long & Liangjun Su & Aman Ullah, 2009. "Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model Variables with Econometric Applications," Working Papers 200908, University of California at Riverside, Department of Economics, revised Jul 2009.

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