Post-J test inference in non-nested linear regression models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 1203-1216 |
Journal / Publication | Science China Mathematics |
Volume | 58 |
Issue number | 6 |
Publication status | Published - 1 Jun 2015 |
Link(s)
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 et al.
In: Science China Mathematics, Vol. 58, No. 6, 01.06.2015, p. 1203-1216.
In: Science China Mathematics, Vol. 58, No. 6, 01.06.2015, p. 1203-1216.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review