Estimating systematic continuous-time trends in recidivism using a non-Gaussian panel data model |
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Authors: | Siem Jan Koopman, Marius Ooms,ré Lucas, Kees van Montfort, Victor van der Geest |
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Affiliation: | Department of Econometrics, VU University Amsterdam, De Boelelaan 1105, NL-1081 HV Amsterdam, The Netherlands; Department of Finance, VU University Amsterdam, De Boelelaan 1105, NL-1081 HV Amsterdam, The Netherlands; Netherlands Institute for the Study of Crime and Law Enforcement, P.O. Box 792, NL-2300 AT Leiden, The Netherlands |
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Abstract: | 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 and (5) missing observations. We adopt a non-Gaussian multivariate state-space model that deals with all 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 behaviour of the juveniles during a period of 13 years, while taking account of significant heterogeneity determined by personality characteristics and initial crime records. |
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Keywords: | non-Gaussian state-space modelling nonlinear panel data model binomial time series odds models recidivism behaviour continuous time modelling |
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