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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| Published in | Journal of applied econometrics (Chichester, England) Vol. 32; no. 3; pp. 600 - 620 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester
Wiley (Variant)
01.04.2017
Wiley Periodicals Inc |
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| Abstract | 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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| AbstractList | This paper proposes an easy-to-use nonparametric indicator for club convergence, or convergence within clusters of countries: it measures whether the modes of the gross domestic product (GDP) per capita distribution become more pronounced over time. Relying on changes in the critical bandwidth for unimodality, the indicator is a dynamic extension of concepts from often-used multimodality tests. Its evolution suggests the new empirical result of a 'millennium peak' in club convergence in the worldwide GDP per capita distribution. The club convergence movements of the 1980s and 1990s, when groups of poor and rich countries converged to two separate points, was followed by a de-clubbing movement after the turn of the millennium. Copyright © 2016 John Wiley & Sons, Ltd. Copyright John Wiley & Sons. Reproduced with Permission. An electronic version of this article is available online at http://www.interscience.wiley.com 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. 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. 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. |
| Author | LIESENFELD, ROMAN VOGLER, JAN RICHARD, JEAN-FRANÇOIS |
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| Cites_doi | 10.2307/1912110 10.1177/0306624X03251124 10.1007/978-94-015-7799-1 10.1007/s10109-011-0158-4 10.1177/0011128712470991 10.2307/1913646 10.1016/j.stamet.2011.02.002 10.1016/S0169-2070(03)00089-X 10.1111/j.1541-0420.2009.01191.x 10.1137/0613024 10.1007/978-3-642-23430-9_92 10.1201/9781420064254 10.1111/j.1538-4632.1972.tb00475.x 10.1080/15230406.2014.893839 10.1016/j.jeconom.2007.02.007 10.1093/sf/76.1.31 10.1086/229381 10.1016/j.regsciurbeco.2012.04.008 10.1111/j.1538-4632.2006.00697.x 10.1086/230997 10.1111/j.1745-9125.2001.tb00932.x 10.1007/978-1-4613-8625-4 10.1111/j.1745-9125.2001.tb00933.x 10.2307/2094589 10.1108/S0731-905320160000037008 10.1016/S0169-2070(03)00092-X 10.1108/S0731-905320160000037009 10.1016/j.csda.2006.08.001 10.1007/978-0-387-09688-9_7 10.1016/j.giq.2013.02.001 10.1016/j.spasta.2014.03.006 10.1007/978-3-642-23430-9_88 10.1177/000271624121700105 10.2307/143141 |
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| Snippet | 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,... 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... 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... This paper proposes an easy-to-use nonparametric indicator for club convergence, or convergence within clusters of countries: it measures whether the modes of... |
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| SubjectTerms | Crime Criminal statistics Econometrics Economic models Pittsburgh Pennsylvania United States Urban areas |
| Title | LIKELIHOOD-BASED INFERENCE AND PREDICTION IN SPATIO-TEMPORAL PANEL COUNT MODELS FOR URBAN CRIMES |
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