cc_byJung, Robert C.Glaser, Stephanie2024-09-032024-09-032022https://hohpublica.uni-hohenheim.de/handle/123456789/16563https://doi.org/10.3390/econometrics10030031This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.engCount data modelsSpatial econometricsSpatial autocorrelationFirm location choice510Article1817219561