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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Econometrics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2225-1146/10/3/31 |
| _version_ | 1797489249743798272 |
|---|---|
| author | Robert C. Jung Stephanie Glaser |
| author_facet | Robert C. Jung Stephanie Glaser |
| author_sort | Robert C. Jung |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:13:48Z |
| format | Article |
| id | doaj.art-344aa3262e42476fa3eb1600de712d84 |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-03-10T00:13:48Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-344aa3262e42476fa3eb1600de712d842023-11-23T15:54:46ZengMDPI AGEconometrics2225-11462022-09-011033110.3390/econometrics10030031Modelling and Diagnostics of Spatially Autocorrelated CountsRobert C. Jung0Stephanie Glaser1Institut für Volkswirtschaftslehre (520K), Computational Science Lab (CSL) Hohenheim, Universität Hohenheim, 70593 Stuttgart, GermanyInstitut für Volkswirtschaftslehre (520K), Universität Hohenheim, 70593 Stuttgart, GermanyThis 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.https://www.mdpi.com/2225-1146/10/3/31count data modelsspatial econometricsspatial autocorrelationfirm location choice |
| spellingShingle | Robert C. Jung Stephanie Glaser Modelling and Diagnostics of Spatially Autocorrelated Counts Econometrics count data models spatial econometrics spatial autocorrelation firm location choice |
| title | Modelling and Diagnostics of Spatially Autocorrelated Counts |
| title_full | Modelling and Diagnostics of Spatially Autocorrelated Counts |
| title_fullStr | Modelling and Diagnostics of Spatially Autocorrelated Counts |
| title_full_unstemmed | Modelling and Diagnostics of Spatially Autocorrelated Counts |
| title_short | Modelling and Diagnostics of Spatially Autocorrelated Counts |
| title_sort | modelling and diagnostics of spatially autocorrelated counts |
| topic | count data models spatial econometrics spatial autocorrelation firm location choice |
| url | https://www.mdpi.com/2225-1146/10/3/31 |
| work_keys_str_mv | AT robertcjung modellinganddiagnosticsofspatiallyautocorrelatedcounts AT stephanieglaser modellinganddiagnosticsofspatiallyautocorrelatedcounts |