Geographically weighted factor analysis: optimal bandwidth selection

Abstract

Geographically weighted models are widely used for analyzing the spatial data. There is a problem of extracting a potentially lower number of unobserved variables while a set of correlated variables is observed. The factor analysis is commonly used to overcome this problem. A bandwidth selection is a main difficulty during the identification a spatial heterogeneity of factor loadings. In the paper an original bandwidth selection criterion is proposed. It is based on the testing the difference between factor loadings of global and geographically weighted model. Using the simulated data it is shown that the criterion proposed allows to define accurately the appropriate number of nearest neighbors.