Semiparametric estimation of sample selection model with Box-Cox transformation
针对样本选择模型中因变量与协变量线性假设易导致模型误设的问题，引入Box-Cox变换并给出半参数估计方法，蒙特卡洛模拟验证有限样本性质，应用于中国劳动力市场工资方程发现对数变换可能无效且存在显著性别工资差异。
The sample selection model is widely used in microeconometrics, especially for the case with nonrandom missing dependent variables. The linear assumption between the potential dependent variable and covariates is often mentioned in the literature. However, nonlinear structures between variables are prevalent in reality, in which case the assumption of linearity can lead to serious model misspecification. To mitigate model misspecification caused by linear assumption, the Box-Cox transformation is applied to the potential dependent variable in the sample selection model, and then the estimation of the corresponding parameters is given under the linear relationship between the transformed variable and covariates. Finite sample properties are investigated by Monte Carlo simulation. Eventually, the new model is applied to analyze the potential wage equation in the labor market in China.The result indicates that ordinary logarithmic transformation of the latent dependent variable is likely to be invalid for this dataset. Furthermore, the findings suggest the presence of a notable gender wage disparity in this particular labor market.