Abstract
Spatial heterogeneity and correlation are both considered in the geographical weighted spatial autoregressive model. At present, this kind of model has aroused the attention of some scholars. For the estimation of the model, the existing research is based on the assumption that the error terms are independent and identically distributed. In this article we use a computationally simple procedure for estimating the model with spatially autoregressive disturbance terms, both the estimates of constant coefficients and variable coefficients are obtained. Finally, we give the large sample properties of the estimators under some ordinary conditions. In addition, application study of the estimation methods involved will be further explored in a separate study.
Acknowledgments
The authors are thankful to the anonymous reviewers and the editor for their valuable comments and constructive suggestions that have led to an improved version of this article.
Notes
1 In the application, there are three kinds of choice of weight function as follow:
Gauss distance weight function: Exponential distance weight function:
Cubic distance weight function:
Where
is the distance between
and
is the density function of standard normal distribution,
is the standard deviation of
is the window width,
is the distance between the observed value
to the nearest neighbor
is the indication function,