Likelihood Based Inference and Prediction in Spatio-temporal Panel Count Models for Urban Crimes
No 5657, Working Paper from Department of Economics, University of Pittsburgh
We develop a panel data count model combined with a latent Gaussian spatio-temporal heterogenousstate process to analyze monthly severe crimes at the census tract level in Pittsburgh,Pennsylvania. Our data set combines Uniform Crime Reporting data with socio-economicdata from the 2000 census. The likelihood of the model is accurately estimated by adaptingrecently developed efficient importance sampling techniques applicable to high-dimensionalspatial models with sparse precision matrices. Our estimation results confirm socio-economicexplanations for crime and, foremost, the broken-windows hypothesis, whereby less severecrimes in a region is a leading indicator for severe crimes. In addition to ML parameterestimates, we compute several other statistics of interest for law enforcement such as elasticities(idiosyncratic, total, short-term as well as long-term) of severe crimes w.r.t. less severecrimes, one-month-ahead out-of-sample forecasts, predictive cumulative distribution functionsand validation test statistics based on these cdf's.
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