this paper we introduce a test for the normality assumption in sample selection models. The test is based from a generalization of a semi-nonparametric maximum likelihood method. In this estimation method, the distribution of the error term is approximated by a Hermite series, with normality a special case. Because all parameters of the model are estimated in bith the simple and more general specification, we can test for normality using the likelihood ratio approach. This test has reasonable power as is shown by a a simulation study. Finally, we apply the generalized semi-nonparametric maximum likelihood 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. Keywords: semi-nonparametric maximum likelihood, density estimation, Hermite series, sample selection.
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