Konferenzbeitrag

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

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.

Language
Englisch

Bibliographic citation
Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2015: Ökonomische Entwicklung - Theorie und Politik - Session: Microeconometric Modelling ; No. D22-V3

Classification
Wirtschaft
Statistical Simulation Methods: General
Econometrics
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models

Event
Geistige Schöpfung
(who)
Vogler, Jan
Liesenfeld, Roman
Richard, Jean-Francois
Event
Veröffentlichung
(when)
2015

Handle
Last update
10.03.2025, 11:43 AM CET

Data provider

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Object type

  • Konferenzbeitrag

Associated

  • Vogler, Jan
  • Liesenfeld, Roman
  • Richard, Jean-Francois

Time of origin

  • 2015

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