Modelling and Diagnostics of Spatially Autocorrelated Counts

Robert Jung () and Stephanie Glaser
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Stephanie Glaser: Institut für Volkswirtschaftslehre (520K), Universität Hohenheim, 70593 Stuttgart, Germany

Econometrics, 2022, vol. 10, issue 3, 1-17

Abstract: 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.

Keywords: count data models; spatial econometrics; spatial autocorrelation; firm location choice (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:10:y:2022:i:3:p:31-:d:913362

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