One-step oracle procedure for semi-parametric spatial autoregressive model and its empirical application to Boston housing price data

Issues concerning spatial dependence among cross-sectional units in econometrics have received more and more attention. Motivated by a Boston housing price data analysis, this paper studies the sparse inference of varying coefficient partially linear spatial autoregressive model, which is quite valuable in econometrics with high-dimensional data. A novel, efficient and convenient one-step variable selection procedure is proposed by using a twofold penalty for simultaneous estimation and variable selection of the parametric components and varying coefficient functions, in which the varying coefficient functions are approximated by the B-spline basis. Under some regularity conditions, asymptotic properties of the resulting estimators are established, including consistency, asymptotic normality and the oracle property. Besides, the optimal choices of the tuning parameters are discussed and a practical iterative algorithm based on the locally quadratic approximation approach is presented for implementation. Finally, extensive numerical simulations and a Boston housing price data analysis are conducted to confirm the finite sample performance and theoretical findings of the new method.