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Article
2022

Modelling and diagnostics of spatially autocorrelated counts

Abstract (English)

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.

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Econometrics, 10 (2022), 3, 31. https://doi.org/10.3390/econometrics10030031. ISSN: 2225-1146
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510 Mathematics

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@article{Jung2022, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16563}, doi = {10.3390/econometrics10030031}, author = {Jung, Robert C. and Glaser, Stephanie}, title = {Modelling and Diagnostics of Spatially Autocorrelated Counts}, journal = {Econometrics}, year = {2022}, volume = {10}, number = {3}, }