A Test for Functional Form Against Nonparametric Alternatives
提出一种检验回归模型是否遗漏非线性关系的统计方法，基于筛估计构建检验统计量，在零假设下渐近服从标准正态分布，模拟显示有限样本性质良好。
A test for neglected nonlinearities in regression models is proposed. The test is of the Davidson-MacKinnon type against an increasingly rich set of non-nested alternatives, and is based on sieve estimation of the alternative model. For the case of a linear parametric model, the test statistic is shown to be asymptotically standard normal under the null, while rejecting with probability going to one if the linear model is misspecified. A small simulation study suggests that the test has adequate finite sample properties, but one must guard against over fitting the nonparametric alternative.