Chen Xinjie1; Fan Yanqin2; Wan Alan3; Zou Guohua1
Source Publicationsciencechinamathematics
AbstractThis 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.
Funding Project[General Research Fund from the Hong Kong Research Grants Council] ; [National Natural Science Foundation of China] ; [Hundred Talents Program of the Chinese Academy of Sciences]
Document Type期刊论文
Recommended Citation
GB/T 7714
Chen Xinjie,Fan Yanqin,Wan Alan,et al. postjtestinferenceinnonnestedlinearregressionmodels[J]. sciencechinamathematics,2015,58(6):1203.
APA Chen Xinjie,Fan Yanqin,Wan Alan,&Zou Guohua.(2015).postjtestinferenceinnonnestedlinearregressionmodels.sciencechinamathematics,58(6),1203.
MLA Chen Xinjie,et al."postjtestinferenceinnonnestedlinearregressionmodels".sciencechinamathematics 58.6(2015):1203.
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