Identification of Spatial Durbin Panel Models
研究了空间杜宾动态面板模型在2SLS和ML估计下的识别问题，蒙特卡洛实验表明遗漏杜宾项会导致显著偏差，而包含无关项则效率损失不大，实证中纳入杜宾项对分析经济增长的国际溢出很重要。
Summary This paper considers identification of spatial Durbin dynamic panel models under 2SLS and ML estimations. We show that the parameters are generally identified via 2SLS moment relations or expected log‐likelihood or quasi‐likelihood functions. Monte Carlo experiments suggest that omitting relevant Durbin terms can result in significant biases in regression estimates, while including an irrelevant Durbin term causes no obvious loss of efficiency. Empirical illustration of the international spillover of economic growth through bilateral trade shows that inclusion of Durbin terms can be important. Copyright © 2015 John Wiley & Sons, Ltd.