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Estimating Systematic Continuous-time Trends in Recidivism using a Non-Gaussian Panel Data Model

Siem Jan Koopman (), Andre Lucas (), Marius Ooms (), Kees van Montfort () and Victor van der Geest
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Kees van Montfort: Vrije Universiteit Amsterdam
Victor van der Geest: Netherlands Institute for the Study of Crime and Law Enforcement (NCSR), Leiden

No 07-027/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This discussion paper led to an article in the Statistica Neerlandica (2008). Vol. 62, issue 1, pages 104-130. We model panel data of crime careers of juveniles from a Dutch Judicial Juvenile Institution. The data are decomposed into a systematic and an individual-specific component, of which the systematic component reflects the general time-varying conditions including the criminological climate. Within a model-based analysis, we treat (1) shared effects of each group with the same systematic conditions, (2) strongly non-Gaussian features of the individual time series, (3) unobserved common systematic conditions, (4) changing recidivism probabilities in continuous time, (5) missing observations. We adopt a non-Gaussian multivariate state space model that deals with all of these issues simultaneously. The parameters of the model are estimated by Monte Carlo maximum likelihood methods. This paper illustrates the methods empirically. We compare continuous-time trends and standard discrete-time stochastic trend specifications. We find interesting common time-variation in the recidivism behavior of the juveniles during a period of 13 years, while taking account of significant heterogeneity determined by personality characteristics and initial crime records.

Keywords: non-Gaussian state space modeling; nonlinear panel data model; binomial time series; recidivism behavior; continuous time modelling (search for similar items in EconPapers)
JEL-codes: C15 C32 C33 D63 (search for similar items in EconPapers)
Date: 2007-03-08
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Journal Article: Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model (2008) Downloads
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