Adaptive lasso variable selection method for semiparametric spatial autoregressive panel data model with random effects

This paper investigates variable selection in semiparametric spatial autoregressive panel data model with random effects. A penalized profile maximum-likelihood method is proposed with adaptive lasso penalty which achieves parameter estimation and variable selection at the same time. Under some regular conditions, we prove the theoretical properties of the estimators, including consistency and oracle property. In addition, we develop a feasible logarithm and carry out numerical simulations to examine the finite sample performance of this method. At last, a real data study about the investment influencing factors of the "Belt and Road" initiative is presented for illustration purpose.