Automatic variable selection for semiparametric spatial autoregressive model
研究了半参数变系数部分线性空间自回归模型的广义矩估计，提出基于平滑阈值估计方程的变量选择方法，自动剔除无关参数和零变系数函数，并通过蒙特卡洛模拟和实证数据验证了方法的有效性。
This article studies the generalized method of moment estimation of semiparametric varying coefficient partially linear spatial autoregressive model. The technique of profile least squares is employed and all estimators have explicit formulas which are computationally convenient. We derive the limiting distributions of the proposed estimators for both parametric and non parametric components. Variable selection procedures based on smooth-threshold estimating equations are proposed to automatically eliminate irrelevant parameters and zero varying coefficient functions. Compared to the alternative approaches based on shrinkage penalty, the new method is easily implemented. Oracle properties of the resulting estimators are established. Large amounts of Monte Carlo simulations confirm our theories and demonstrate that the estimators perform reasonably well in finite samples. We also apply the novel methods to an empirical data analysis.