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Institut für Volkswirtschaftslehre

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/24

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    Modelling and diagnostics of spatially autocorrelated counts
    (2022) Jung, Robert C.; Glaser, Stephanie
    This 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.