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.
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Chen, X., Fan, Y., Wan, A. et al. Post-J test inference in non-nested linear regression models. Sci. China Math. 58, 1203–1216 (2015). https://doi.org/10.1007/s11425-014-4935-7
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DOI: https://doi.org/10.1007/s11425-014-4935-7