An Alternative Parametric Estimation of Sample Selection Model: An Application to Car Ownership and Car Expense Choi, Phil-Sun; Min, In-Sik;
In a parametric sample selection model, the distribution assumption is critical to obtain consistent estimates. Conventionally, the normality assumption has been adopted for both error terms in selection and main equations of the model. The normality assumption, however, may excessively restrict the true underlying distribution of the model. This study introduces the -normal distribution into the error distribution of a sample selection model. The -normal distribution can accommodate a wide range of skewness and kurtosis compared to the normal distribution. It also includes the normal distribution as a limiting distribution. Moreover, the -normal distribution can be easily extended to multivariate dimensions. We provide the log-likelihood function and expected value formula based on a bivariate -normal distribution in a sample selection model. The results of simulations indicate the -normal model outperforms the normal model for the consistency of estimators. As an empirical application, we provide the sample selection model for car ownership and a car expense relationship.