Likelihood-Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes
| Autor: | Roman Liesenfeld, Jan Vogler, Jean-François Richard |
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| Rok vydání: | 2016 |
| Předmět: |
Estimation
Economics and Econometrics Computer science Validation test 05 social sciences Law enforcement Inference Poison control 01 natural sciences Crime reporting 010104 statistics & probability Economic indicator 0502 economics and business Statistics Econometrics 050207 economics 0101 mathematics Social Sciences (miscellaneous) Importance sampling |
| Zdroj: | Journal of Applied Econometrics. 32:600-620 |
| ISSN: | 0883-7252 |
| DOI: | 10.1002/jae.2534 |
| Popis: | 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. Copyright © 2016 John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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