On the estimation and testing of mixed geographically weighted regression models

Mixed geographically weighted regression (MGWR) model is a useful technique to explore spatial non-stationarity by allowing that some coefficients of the explanatory variables are constant and others are spatially varying, but its estimation and inference have not been systematically studied. This paper is concerned with estimation and testing of the model when there are certain linear constraints on the elements of constant coefficients. We propose a constrained two-step technique for estimating the constant coefficients and spatial varying coefficients, and develop a test procedure for the validity of the linear constraints. Finally, some simulations are conducted to examine the performance of our proposed procedure and the results are satisfactory.

► We consider a constrained mixed geographically weighted regression model. ► We propose a constrained two-step estimating approach. ► We develop a test procedure for the validity of the linear constraints. ► Some simulations are conducted and the results are satisfactory.