Modelling and Diagnostics of Spatially Autocorrelated Counts
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 observatio...
| الحاوية / القاعدة: | Econometrics |
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| المؤلفون الرئيسيون: | , |
| التنسيق: | مقال |
| اللغة: | الإنجليزية |
| منشور في: |
MDPI AG
2022-09-01
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/2225-1146/10/3/31 |
| الملخص: | 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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| تدمد: | 2225-1146 |