Post-J test inference in non-nested linear regression models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1203-1216
Journal / PublicationScience China Mathematics
Volume58
Issue number6
Publication statusPublished - 1 Jun 2015

Abstract

This paper considers the post-J test inference in non-nested linear regression models. Post-J test inference means that the inference problem is considered by taking the first stage J test into account. We first propose a post-J test estimator and derive its asymptotic distribution. We then consider the test problem of the unknown parameters, and a Wald statistic based on the post-J test estimator is proposed. A simulation study shows that the proposed Wald statistic works perfectly as well as the two-stage test from the view of the empirical size and power in large-sample cases, and when the sample size is small, it is even better. As a result, the new Wald statistic can be used directly to test the hypotheses on the unknown parameters in non-nested linear regression models.

Research Area(s)

  • non-nested linear regression, post-J test, Wald statistic

Citation Format(s)

Post-J test inference in non-nested linear regression models. / Chen, XinJie; Fan, YanQin; Wan, Alan; Zou, GuoHua.

In: Science China Mathematics, Vol. 58, No. 6, 01.06.2015, p. 1203-1216.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal