  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 alpha-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.
