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Likelihood‐Based Inference and Prediction in Spatio‐Temporal Panel Count Models for Urban Crimes
Author(s) -
Liesenfeld Roman,
Richard JeanFrançois,
Vogler Jan
Publication year - 2016
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2534
Subject(s) - inference , econometrics , panel data , law enforcement , sample (material) , statistics , sampling (signal processing) , census tract , statistical inference , estimation , count data , computer science , census , economics , law , mathematics , artificial intelligence , demography , political science , sociology , population , chemistry , management , poisson distribution , filter (signal processing) , chromatography , computer vision
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.