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Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model
Author(s) -
Koopman Siem Jan,
Ooms Marius,
Lucas André,
Montfort Kees van,
Van Der Geest Victor
Publication year - 2008
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2007.00375.x
Subject(s) - recidivism , econometrics , statistics , gaussian , component (thermodynamics) , random effects model , mathematics , meta analysis , psychology , criminology , medicine , physics , quantum mechanics , thermodynamics
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.