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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)

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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Bibliographic Info

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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Web page: http://www.tinbergen.nl

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

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  1. Joon Y. Park & Peter C. B. Phillips, 1999. "Nonstationary Binary Choice," Working Paper Series no5, Institute of Economic Research, Seoul National University.
  2. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
  3. H. Naci Mocan & Hope Corman, 2000. "A Time-Series Analysis of Crime, Deterrence, and Drug Abuse in New York City," American Economic Review, American Economic Association, vol. 90(3), pages 584-604, June.
  4. Steven D. Levitt, 1995. "Using Electoral Cycles in Police Hiring to Estimate the Effect of Policeon Crime," NBER Working Papers 4991, National Bureau of Economic Research, Inc.
  5. Peter Schmidt & Ann Dryden Witte, 1987. "Predicting Criminal Recidivism Using "Split Population" Survival Time Models," NBER Working Papers 2445, National Bureau of Economic Research, Inc.
  6. Cornwell, Christopher & Trumbull, William N, 1994. "Estimating the Economic Model of Crime with Panel Data," The Review of Economics and Statistics, MIT Press, vol. 76(2), pages 360-66, May.
  7. Jukka Nyblom & Andrew Harvey, 2001. "Testing against smooth stochastic trends," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 415-429.
  8. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
  9. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, September.
  10. Donald W.K. Andrews, 1999. "Testing When a Parameter Is on the Boundary of the Maintained Hypothesis," Cowles Foundation Discussion Papers 1229, Cowles Foundation for Research in Economics, Yale University.
  11. Bergstrom, A.R., 1984. "Continuous time stochastic models and issues of aggregation over time," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 20, pages 1145-1212 Elsevier.
  12. Johan Oud & Robert Jansen, 2000. "Continuous time state space modeling of panel data by means of sem," Psychometrika, Springer, vol. 65(2), pages 199-215, June.
  13. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
  14. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. Suncica Vujic & Siem Jan Koopman & Jacques J. F. Commandeur, 2012. "Economic Trends and Cycles in Crime: A Study for England and Wales," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 232(6), pages 652-677, November.

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