Modelling and Diagnostics of Spatially Autocorrelated Counts

This paper proposes a new spatial lag regression model which addresses global autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the correlation parameter as measurement impact neighbouring observations on conditional expectation It allows for flexible likelihood-based inference based different distributional assumptions using standard numerical procedures. In addition, we advocate use data-coherent diagnostic tools in count models. The application revisits data set location choice single unit start-up firms manufacturing industry US.