A study on geographically weighted spatial autoregression models with spatial autoregressive disturbances

Xiaozhi Peng, Hecheng Wu and Ling Ma

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 21, 5235-5251

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.

Date: 2020
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DOI: 10.1080/03610926.2019.1615507

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