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Use of Modified Profile Likelihood for Improved Tests of Constancy of Variance in Regression

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  • Jeffrey S. Simonoff
  • Chih‐Ling Tsai

Abstract

Non‐constant variance (heteroscedasticity) is common in regression data, and many tests have been proposed for detecting it. This paper shows that the properties of likelihood‐based tests can be improved by using the modified profile likelihood of Cox and Reid. A modified likelihood ratio test and modified score tests are derived, and both theoretical and intuitive justifications are given for the improved properties of the tests. The results of a Monte Carlo study are mentioned, which show that, whereas the ordinary likelihood ratio test can be very anticonservative, the modified test holds its null size well and is more powerful than the other tests. For non‐normal error distributions. Studentized tests hold their size well (without being overconservative), even for long‐tailed error distributions. Under short‐tailed error distributions, likelihood ratio or Studentized score tests are most powerful, depending on the degree of heteroscedasticity. The modified versions of the score tests consistently outperform the unmodified versions. The use of these tests is demonstrated through analysis of data on the volatility of stock prices.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:43:y:1994:i:2:p:357-370
    DOI: 10.2307/2986026
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    Cited by:

    1. Ferrari, Silvia L. P. & Cribari-Neto, Francisco, 2002. "Corrected modified profile likelihood heteroskedasticity tests," Statistics & Probability Letters, Elsevier, vol. 57(4), pages 353-361, May.
    2. 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.
    3. Zhu, Xuehu & Guo, Xu & Lin, Lu & Zhu, Lixing, 2015. "Heteroscedasticity checks for single index models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 41-55.
    4. Xie, Feng-Chang & Wei, Bo-Cheng & Lin, Jin-Guan, 2009. "Homogeneity diagnostics for skew-normal nonlinear regression models," Statistics & Probability Letters, Elsevier, vol. 79(6), pages 821-827, March.
    5. Feng-Chang Xie & Jin-Guan Lin & Bo-Cheng Wei, 2010. "Testing for varying zero-inflation and dispersion in generalized Poisson regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1509-1522.
    6. Xie, Feng-Chang & Lin, Jin-Guan & Wei, Bo-Cheng, 2009. "Diagnostics for skew-normal nonlinear regression models with AR(1) errors," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4403-4416, October.
    7. Mariana C. Araújo & Audrey H. M. A. Cysneiros & Lourdes C. Montenegro, 2020. "Improved heteroskedasticity likelihood ratio tests in symmetric nonlinear regression models," Statistical Papers, Springer, vol. 61(1), pages 167-188, February.
    8. Cordeiro, Gauss M., 2008. "Corrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 28(1), May.
    9. Wong, Heung & Liu, Feng & Chen, Min & Ip, Wai Cheung, 2009. "Empirical likelihood based diagnostics for heteroscedasticity in partial linear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3466-3477, July.
    10. Chun-Zheng Cao & Jin-Guan Lin & Li-Xing Zhu, 2010. "Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors," Statistical Papers, Springer, vol. 51(4), pages 813-836, December.
    11. Zhu, Zhongyi & Fung, Wing K., 2004. "Variance component testing in semiparametric mixed models," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 107-118, October.
    12. Zhu, Xuehu & Chen, Fei & Guo, Xu & Zhu, Lixing, 2016. "Heteroscedasticity testing for regression models: A dimension reduction-based model adaptive approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 263-283.
    13. Xiaohui Liu & Zhizhong Wang & Xuemei Hu, 2011. "Testing heteroscedasticity in partially linear models with missing covariates," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 321-337.

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