Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients
针对每个位置（或面板）的标量系数不同的时空模型，提出广义Yule-Walker估计方法，解决内生性问题，并在样本量和位置数趋于无穷时建立渐近理论，适用于空间自回归面板数据模型。
We consider a class of spatio-temporal models which extend popular econometric spatial autoregressive panel data models by allowing the scalar coefficients for each location (or panel) different from each other. To overcome the innate endogeneity, we propose a generalized Yule–Walker estimation method which applies the least squares estimation to a Yule–Walker equation. The asymptotic theory is developed under the setting that both the sample size and the number of locations (or panels) tend to infinity under a general setting for stationary and α-mixing processes, which includes spatial autoregressive panel data models driven by i.i.d. innovations as special cases. The proposed methods are illustrated using both simulated and real data.