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

S.J. Koopman, Andre Lucas, C.A.G.M. van Montfort, M. Ooms, W. van der Geest

Research output: Contribution to JournalArticleAcademicpeer-review

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. © 2007 The Authors. Journal compilation 2007 VVS.
Original languageEnglish
Pages (from-to)104-130
JournalStatistica Neerlandica. Journal of the Netherlands Society for Statistics and Operations Research
Volume62
Issue number1
DOIs
Publication statusPublished - 2008

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