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Article
2022
Modelling and diagnostics of spatially autocorrelated counts
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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English
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510 Mathematics
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Sustainable Development Goals
BibTeX
@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},
}