Modelling and diagnostics of spatially autocorrelated counts

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dc.contributor.authorJung, Robert C.
dc.contributor.authorGlaser, Stephanie
dc.date.accessioned2024-09-03T14:03:42Z
dc.date.available2024-09-03T14:03:42Z
dc.date.issued2022de
dc.description.abstractThis 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.en
dc.identifier.swb1817219561
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16563
dc.identifier.urihttps://doi.org/10.3390/econometrics10030031
dc.language.isoengde
dc.rights.licensecc_byde
dc.source2225-1146de
dc.sourceEconometrics; Vol. 10, No. 3 (2022) 31de
dc.subjectCount data models
dc.subjectSpatial econometrics
dc.subjectSpatial autocorrelation
dc.subjectFirm location choice
dc.subject.ddc510
dc.titleModelling and diagnostics of spatially autocorrelated countsen
dc.type.diniArticle
dcterms.bibliographicCitationEconometrics, 10 (2022), 3, 31. https://doi.org/10.3390/econometrics10030031. ISSN: 2225-1146
dcterms.bibliographicCitation.issn2225-1146
dcterms.bibliographicCitation.issue3
dcterms.bibliographicCitation.journaltitleEconometrics
dcterms.bibliographicCitation.volume10
local.export.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}, }
local.export.bibtexAuthorJung, Robert C. and Glaser, Stephanie
local.export.bibtexKeyJung2022
local.export.bibtexType@article

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