Search

In this paper, we consider the variable selection for a class of semiparametric spatial autoregressive models. By using orthogonal projection technique, we propose a new orthogonality-based variable selection procedure, which can select important covariates, and can identify the significance of spatial effects simultaneously. The consistency of the proposed variable selection procedure and the convergence rate of the resulting estimators are derived under some regular conditions. Furthermore, some simulation studies are carried out to examine the finite sample performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we first present partially linear single-index spatial autoregressive model and propose its profile maximum likelihood estimators (PMLE). Subsequently, consistency and asymptotic normality of the estimators for parameters and unknown link function are derived under some regular conditions. Thirdly, Monte Carlo simulations are used to investigate the performances of these estimators in finite sample cases. Finally, the proposed method is illustrated with the real data set of Boston Housing Price. [ABSTRACT FROM AUTHOR]