LIKELIHOOD-BASED INFERENCE AND PREDICTION IN SPATIO-TEMPORAL PANEL COUNT MODELS FOR URBAN CRIMES

We develop a panel count model with a latent spatio-temporal heterogeneous state process for monthly severe crimes at the census-tract level in Pittsburgh, Pennsylvania. Our dataset combines Uniform Crime Reporting data with socio-economic data. The likelihood is estimated by efficient importance sa...

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Bibliographic Details
Published inJournal of applied econometrics (Chichester, England) Vol. 32; no. 3; pp. 600 - 620
Main Authors LIESENFELD, ROMAN, RICHARD, JEAN-FRANÇOIS, VOGLER, JAN
Format Journal Article
LanguageEnglish
Published Chichester Wiley (Variant) 01.04.2017
Wiley Periodicals Inc
Subjects
Crime
Criminal statistics
Econometrics
Economic models
Pittsburgh Pennsylvania
United States
Urban areas
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Summary:We develop a panel count model with a latent spatio-temporal heterogeneous state process for monthly severe crimes at the census-tract level in Pittsburgh, Pennsylvania. Our dataset combines Uniform Crime Reporting data with socio-economic data. The likelihood is estimated by efficient importance sampling techniques for high-dimensional spatial models. Estimation results confirm the broken-windows hypothesis whereby less severe crimes are leading indicators for severe crimes. In addition to ML parameter estimates, we compute several other statistics of interest for law enforcement such as spatio-temporal elasticities of severe crimes with respect to less severe crimes, out-of-sample forecasts, predictive distributions and validation test statistics.
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ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2534