IDEAS home Printed from
   My bibliography  Save this article

Tests of heteroscedasticity and correlation in multivariate t regression models with AR and ARMA errors


  • Jin-Guan Lin
  • Li-Xing Zhu
  • Chun-Zheng Cao
  • Yong Li


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.

Suggested Citation

  • Jin-Guan Lin & Li-Xing Zhu & Chun-Zheng Cao & Yong Li, 2011. "Tests of heteroscedasticity and correlation in multivariate t regression models with AR and ARMA errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1509-1531, August.
  • Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1509-1531
    DOI: 10.1080/02664763.2010.515301

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Murray Aitkin, 1987. "Modelling Variance Heterogeneity in Normal Regression Using GLIM," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 332-339, November.
    2. Epps, Thomas W & Epps, Mary Lee, 1977. "The Robustness of Some Standard Tests for Autocorrelation and Heteroskedasticity When Both Problems Are Present," Econometrica, Econometric Society, vol. 45(3), pages 745-753, April.
    3. Borjas, George J. & Freeman, Richard B. (ed.), 1992. "Immigration and the Work Force," National Bureau of Economic Research Books, University of Chicago Press, number 9780226066332, March.
    4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    5. Bo-Cheng Wei & Jian-Qing Shi & Wing-Kam Fung & Yue-Qing Hu, 1998. "Testing for Varying Dispersion in Exponential Family Nonlinear Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(2), pages 277-294, June.
    6. Lin, Jin-Guan & Wei, Bo-cheng, 2006. "Approximate power of score test for variance heterogeneity under local alternatives in nonlinear models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3179-3198, July.
    7. Barroso, Lúcia P. & Cordeiro, Gauss M., 2005. "Bartlett corrections in heteroskedastic t regression models," Statistics & Probability Letters, Elsevier, vol. 75(2), pages 86-96, November.
    8. Jeffrey S. Simonoff & Chih‐Ling Tsai, 1994. "Use of Modified Profile Likelihood for Improved Tests of Constancy of Variance in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(2), pages 357-370, June.
    9. Jin-Guan Lin & Bo-Cheng Wei & Nan-Song Zhang, 2004. "Varying Dispersion Diagnostics for Inverse Gaussian Regression Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(10), pages 1157-1170.
    10. Cysneiros, Francisco José A. & Paula, Gilberto A. & Galea, Manuel, 2007. "Heteroscedastic symmetrical linear models," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1084-1090, June.
    11. Singh, Radhey S., 1988. "Estimation of error variance in linear regression models with errors having multivariate student-t distribution with unknown degrees of freedom," Economics Letters, Elsevier, vol. 27(1), pages 47-53.
    12. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1509-1531. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.