Tests of heteroscedasticity and correlation in multivariate t regression models with AR and ARMA errors |
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| Authors: | Jin-Guan Lin Li-Xing Zhu Chun-Zheng Cao Yong Li |
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| Institution: | 1. Department of Mathematics , Southeast University , Nanjing, 210096, People's Republic of China;2. Department of Mathematics , Hong Kong Baptist University , People's Republic of China;3. Department of Mathematics , Southeast University , Nanjing, 210096, People's Republic of China;4. College of Math &5. Physics , University of Information Science &6. Technology , Nanjing, 210044, People's Republic of China;7. School of Business , Sun Yat-Sen University , Guangzhou, 510275, People's Republic of China |
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| Abstract: | Heteroscedasticity checking in regression analysis plays an important role in modelling. It is of great interest when random errors are correlated, including autocorrelated and partial autocorrelated errors. In this paper, we consider multivariate t linear regression models, and construct the score test for the case of AR(1) errors, and ARMA(s,d) errors. The asymptotic properties, including asymptotic chi-square and approximate powers under local alternatives of the score tests, are studied. Based on modified profile likelihood, the adjusted score test is also developed. The finite sample performance of the tests is investigated through Monte Carlo simulations, and also the tests are illustrated with two real data sets. |
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| Keywords: | adjusted score test approximate local powers autocorrelation heteroscedasticity score test multivariate t regression models simulation studies |
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