Semi‐parametric Regression under Model Uncertainty: Economic Applications
提出贝叶斯半参数回归与随机搜索变量选择相结合的方法，同时处理变量选择和函数形式两种模型不确定性，用于识别稳健的线性和非线性效应，并应用于住房支付意愿和跨国增长回归。
Abstract Economic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi‐parametric regression methods in combination with stochastic search variable selection can be used to address two model uncertainties simultaneously: (i) the uncertainty with respect to the variables which should be included in the model and (ii) the uncertainty with respect to the functional form of their effects. The presented approach enables the simultaneous identification of robust linear and nonlinear effects. The additional insights gained are illustrated on applications in empirical economics, namely willingness to pay for housing, and cross‐country growth regression.