Universität zu Köln

Liesenfeld, Roman, Richard, Jean-Francois and Vogler, Jan (2017). Likelihood-Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes. J. Appl. Econom., 32 (3). S. 600 - 621. HOBOKEN: WILEY. ISSN 1099-1255

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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. Copyright (c) 2016 John Wiley & Sons, Ltd.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Liesenfeld, Roman UNSPECIFIED UNSPECIFIED UNSPECIFIED Richard, Jean-Francois UNSPECIFIED UNSPECIFIED UNSPECIFIED Vogler, Jan UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-235594
DOI:	10.1002/jae.2534
Journal or Publication Title:	J. Appl. Econom.
Volume:	32
Number:	3
Page Range:	S. 600 - 621
Date:	2017
Publisher:	WILEY
Place of Publication:	HOBOKEN
ISSN:	1099-1255
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language HOMICIDE RATES; SPATIAL DYNAMICS; TIME-SERIES; VIOLENCE; RISK Multiple languages Economics; Social Sciences, Mathematical Methods Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/23559