Estimating and Forecasting with a Dynamic Spatial Panel Data Model*
针对带空间相关误差的动态空间滞后面板数据模型，提出一种混合非空间和空间工具的广义矩估计方法，并推导线性预测公式，通过蒙特卡洛模拟比较其与多种估计量的表现，最后应用于新经济地理学。
Abstract This study focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006) , a dynamic spatial generalized method of moments (GMM) estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the spatial autoregressive (SAR) error model. The main idea is to mix non‐spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non‐spatial estimators and illustrate our approach with an application to new economic geography.