Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/306649 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Spatial Econometrics [ISSN:] 2662-298X [Volume:] 3 [Issue:] 1 [Article No.:] 2 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2022
Publisher: 
Springer International Publishing, Cham
Abstract: 
The steadily growing access to high-quality spatio-temporal crime count data with a high level of spatial detail allows to uncover interesting relationships between crime types within and between small regional units. Data coherent forecasting of such counts has to take the integer and non-negative nature of the data into account. Spatial panel data models that meet the criterion of coherency are relatively sparse. This paper proposes a new spatial panel regression framework with fixed effects to overcome these shortcomings. Depending on whether time dynamic effects are included in the model specification, estimation and inference are based either on a pseudo maximum likelihood method or on quasi-differenced generalized methods of moments. The models' usefulness is demonstrated in a forecasting exercise of monthly crime counts at census tract level from Pittsburgh, Pennsylvania.
Subjects: 
Count data
Spatial panel models
Fixed effects
Predictive modeling
JEL: 
C33
C53
K42
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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