Testing the Normality Assumption in the Sample Selection Model With an Application to Travel Demand
提出一种基于半非参数最大似然法的检验，用于判断样本选择模型中的误差项是否服从正态分布，并通过模拟验证其效力，最后应用于汽车拥有与使用模型。
In this paper we introduce a test for the normality assumption in the sample selection model. The test is based on a generalization of a semi-nonparametric maximum likelihood method. In this estimation method, the distribution of the error terms is approximated by a Hermite series, with normality as a special case. Because all parameters of the model are estimated both under normality and in the more general specification, we can test for normality using the likeli-hood ratio approach. This test has reasonable power as is shown by a simulation study. Finally, we apply the generalized semi-nonparametric maximum likeli-hood estimation method and the normality test to a model of car ownership and car use. The assumption of normal distributed error terms is rejected and we provide estimates of the sample selection model that are consistent.