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Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model

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  • Siem Jan Koopman
  • Marius Ooms
  • André Lucas
  • Kees van Montfort
  • Victor Van Der Geest

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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  • Siem Jan Koopman & Marius Ooms & André Lucas & Kees van Montfort & Victor Van Der Geest, 2008. "Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 104-130, February.
    • Handle: RePEc:bla:stanee:v:62:y:2008:i:1:p:104-130
    DOI: 10.1111/j.1467-9574.2007.00375.x
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    Cited by:

    1. Vujić, Sunčica & Commandeur, Jacques J.F. & Koopman, Siem Jan, 2016. "Intervention time series analysis of crime rates: The case of sentence reform in Virginia," Economic Modelling, Elsevier, vol. 57(C), pages 311-323.
    2. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
    3. Vujić Sunčica & Koopman Siem Jan & Commandeur J.F., 2012. "Economic Trends and Cycles in Crime: A Study for England and Wales," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(6), pages 652-677, December.
    4. Suncica Vujic & Jacques Commandeur & Siem Jan Koopman, 2012. "Structural Intervention Time Series Analysis of Crime Rates: The Impact of Sentence Reform in Virginia," Tinbergen Institute Discussion Papers 12-007/4, Tinbergen Institute.

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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