Bayesian Inference in Spatial Sample Selection Models
研究了空间相关扰动项下样本选择模型的贝叶斯估计方法，设计了基于数据增广的MCMC算法，通过处理选择方程中扰动项方差这一未识别参数来实现其他参数的识别，并用模拟和实证展示了算法效果。
Abstract In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter – the variance of the disturbance term in the selection equation – is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study and an empirical application.