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

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

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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, (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.

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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 07-027/4.

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Date of creation: 08 Mar 2007
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Handle: RePEc:dgr:uvatin:20070027

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Related research
Keywords: non-Gaussian state space modeling; nonlinear panel data model; binomial time series; recidivism behavior; continuous time modelling;

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Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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