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

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

This discussion paper led to an article in the Statistica Neerlandica (2008). Vol. 62, issue 1, pages 104-130. 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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Article provided by Netherlands Society for Statistics and Operations Research in its journal Statistica Neerlandica.

Volume (Year): 62 (2008)
Issue (Month): 1 ()
Pages: 104-130

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Handle: RePEc:bla:stanee:v:62:y:2008:i:1:p:104-130
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  1. Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998. "Statistical Algorithms for Models in State Space Using SsfPack 2.2," Discussion Paper 1998-141, Tilburg University, Center for Economic Research.
  2. 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.
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  5. Levitt, Steven D, 1997. "Using Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime," American Economic Review, American Economic Association, vol. 87(3), pages 270-90, June.
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  7. Gary S. Becker, 1968. "Crime and Punishment: An Economic Approach," Journal of Political Economy, University of Chicago Press, vol. 76, pages 169.
  8. 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.
  9. 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.
  10. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
  11. 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.
  12. Catrien C.J.H. Bijleveld & Ab Mooijaart, 2003. "Latent Markov Modelling of Recidivism Data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(3), pages 305-320.
  13. 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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  15. 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.
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