Asymptotic distribution-free tests for semiparametric regressions with dependent data
提出一种新方法，为可能相依的数据构造半参数回归模型的渐近分布自由检验，通过变换误差分布差异得到已知临界值的检验统计量，蒙特卡洛模拟和实证应用表明有限样本表现良好。
This article proposes a new general methodology for constructing nonparametric and semiparametric Asymptotically Distribution-Free (ADF) tests for semiparametric hypotheses in regression models for possibly dependent data coming from a strictly stationary process. Classical tests based on the difference between the estimated distributions of the restricted and unrestricted regression errors are not ADF. In this article, we introduce a novel transformation of this difference that leads to ADF tests with well-known critical values. The general methodology is illustrated with applications to testing for parametric models against nonparametric or semiparametric alternatives, and semiparametric constrained mean–variance models. Several Monte Carlo studies and an empirical application show that the finite sample performance of the proposed tests is satisfactory in moderate sample sizes.