Likelihood based inference and prediction in spatio-temporal panel count models for urban crimes

Jan Vogler, Roman Liesenfeld and Jean-Francois Richard

VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy from Verein für Socialpolitik / German Economic Association

Abstract: PRELIMINARY DRAFT We discuss maximum likelihood (ML) analysis for panel count data models, in which the observed counts are linked via a measurement density to a latent Gaussian process with spatial as well as temporal dynamics and random effects. For likelihood evaluation requiring high-dimensional integration we rely upon Efficient Importance Sampling (EIS). The algorithm we develop extends existing EIS implementations by constructing importance sampling densities, which closely approximate the nontrivial spatio-temporal correlation structure under dynamic spatial panel models. In order to make this high-dimensional approximation computationally feasible, our EIS implementation exploits the typical sparsity of spatial precision matrices in such a way that all the high-dimensional matrix operations it requires can be performed using computationally fast sparse matrix functions. We use the proposed sparse EIS-ML approach for an extensive empirical study analyzing the socio-demographic determinants and the space-time dynamics of urban crime in Pittsburgh, USA, between 2008 and 2013 for a panel of monthly crime rates at census-tract level.

JEL-codes: C01 C15 C23 (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm and nep-ure
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Related works:
Journal Article: Likelihood‐Based Inference and Prediction in Spatio‐Temporal Panel Count Models for Urban Crimes (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc15:113131

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