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Efficient inference for regression models requires that heteroscedasticity be taken into account if it exists. For partially linear regression models, however, the problem of detecting heteroscedasticity has received very little attention. The aim of this paper is to propose a test of...
Persistent link: https://www.econbiz.de/10005211790
Weak convergence results are obtained in this paper for the residual empirical processes in linear models, when the normal error assumption is replaced by a number of other error assumptions such as the Gamma family and the inverse Gaussian family. Application to asymptotic power calculations...
Persistent link: https://www.econbiz.de/10005211954
This paper uses the wild bootstrap technique in the estimation of a heteroscedastic partially linear regression model. We show that this approach provides reliable approximation to the asymptotic distribution of the semiparametric least-square estimators of the linear regression coefficients and...
Persistent link: https://www.econbiz.de/10005223364
This paper studies the estimation of a varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model [Fan and Huang, Manuscript, University of North Carolina, Chapel Hill, USA, 2002]. We focus on...
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In this paper jackknifing technique is examined for functions of the parametric component in a partially linear regression model with serially correlated errors. By deleting partial residuals a jackknife-type estimator is proposed. It is shown that the jackknife-type estimator and the usual...
Persistent link: https://www.econbiz.de/10005093899
The authors study a heteroscedastic partially linear regression model and develop an inferential procedure for it. This includes a test of heteroscedasticity, a two-step estimator of the heteroscedastic variance function, semiparametric generalized least-squares estimators of the parametric and...
Persistent link: https://www.econbiz.de/10005093907