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Heteroscedasticity diagnostics in varying-coefficient partially linear regression models and applications in analyzing Boston housing data

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  • Jin-Guan Lin
  • Yan-Yong Zhao
  • Hong-Xia Wang

Abstract

It is important to detect the variance heterogeneity in regression model because efficient inference requires that heteroscedasticity is taken into consideration if it really exists. For the varying-coefficient partially linear regression models, however, the problem of detecting heteroscedasticity has received very little attention. In this paper, we present two classes of tests of heteroscedasticity for varying-coefficient partially linear regression models. The first test statistic is constructed based on the residuals, in which the error term is from a normal distribution. The second one is motivated by the idea that testing heteroscedasticity is equivalent to testing pseudo-residuals for a constant mean. Asymptotic normality is established with different rates corresponding to the null hypothesis of homoscedasticity and the alternative. Some Monte Carlo simulations are conducted to investigate the finite sample performance of the proposed tests. The test methodologies are illustrated with a real data set example.

Suggested Citation

  • Jin-Guan Lin & Yan-Yong Zhao & Hong-Xia Wang, 2015. "Heteroscedasticity diagnostics in varying-coefficient partially linear regression models and applications in analyzing Boston housing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(11), pages 2432-2448, November.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2432-2448
    DOI: 10.1080/02664763.2015.1043623
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