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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2015: Ökonomische Entwicklung - Theorie und Politik - Session: Microeconometric Modelling ; No. D22-V3
- Klassifikation
-
Wirtschaft
Statistical Simulation Methods: General
Econometrics
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Vogler, Jan
Liesenfeld, Roman
Richard, Jean-Francois
- Ereignis
-
Veröffentlichung
- (wann)
-
2015
- Handle
- Letzte Aktualisierung
- 10.03.2025, 10:43 UTC
Datenpartner
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Objekttyp
- Konferenzbeitrag
Beteiligte
- Vogler, Jan
- Liesenfeld, Roman
- Richard, Jean-Francois
Entstanden
- 2015
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