GMM estimation of partially linear additive spatial autoregressive model
研究了部分线性可加空间自回归模型的估计方法，用局部线性估计逼近非参数项，提出广义矩估计量，推导了大样本性质，并通过蒙特卡洛模拟和房价数据分析验证了方法。
This paper focuses on studying the estimation method of partially linear additive spatial autoregressive model (PLASARM) by combining both parametric and nonparametric terms. With the nonparametric functions approximated by local linear estimator, the generalized method of moment (GMM) estimators is proposed. The large sample properties of the estimators are derived for the case with a single nonparametric term and extended to an arbitrary number of nonparametric additive terms under some mild conditions. The small sample performance for our estimators is assessed by Monte Carlo simulation. In addition, the proposed method is used to analyse the forces of Chinese housing price.