TY - JOUR
T1 - Estimating Systematic Continuous-time Trends in Recidivism Using a Non-Gaussian Panel Data Model
AU - Koopman, S.J.
AU - Lucas, Andre
AU - van Montfort, C.A.G.M.
AU - Ooms, M.
AU - van der Geest, W.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
U2 - 10.1111/j.1467-9574.2007.00375.x
DO - 10.1111/j.1467-9574.2007.00375.x
M3 - Article
VL - 62
SP - 104
EP - 130
JO - Statistica Neerlandica. Journal of the Netherlands Society for Statistics and Operations Research
JF - Statistica Neerlandica. Journal of the Netherlands Society for Statistics and Operations Research
SN - 0039-0402
IS - 1
ER -
