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Using Markov-Switching Models to Identify the Link between Unemployment and Criminality


  • Firouz Fallahi

    (Department of Economics, University of Ottawa)

  • Gabriel Rodríguez

    () (Department of Economics, University of Ottawa)


Using Markov Switching Autoregressive models the behaviour of four crime variables and unemployment rate during the period of study is investigated and different regimes for each variable determined. Using some nonparametric measures such as the Concordance Index (Harding and Pagan, 2002) and Independence of Chronologies (Bodman and Crosby, 2005), among others, the independency of cycles of unemployment rate and crime variables is tested. The results of this stage show that there is no relationship between unemployment rate and burglary and motor. However, for larceny and robbery the results are mixed and inconclusive. At the second stage, Markov Switching Vector Autoregressive models are also used to determine the states for both unemployment rate and each one of crime variable simultaneously. The results of this stage show that the effect of unemployment rate on larceny and motor depends on the state of the variables. For larceny this effect is either positive or null, and for motor it fluctuates among negative, null, and positive. Also the result shows that regardless of the state of the variables, the effect of unemployment on burglary and robbery is negative and null, respectively.

Suggested Citation

  • Firouz Fallahi & Gabriel Rodríguez, 2007. "Using Markov-Switching Models to Identify the Link between Unemployment and Criminality," Working Papers 0701E, University of Ottawa, Department of Economics.
  • Handle: RePEc:ott:wpaper:0701e

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    References listed on IDEAS

    1. McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, vol. 31(3), pages 341-362, June.
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    3. Philip Bodman & Mark Crosby, 2005. "Are business cycles independent in the G7?," International Economic Journal, Taylor & Francis Journals, vol. 19(4), pages 483-499.
    4. Hess, Gregory D. & Iwata, Shigeru, 1997. "Asymmetric persistence in GDP? A deeper look at depth," Journal of Monetary Economics, Elsevier, vol. 40(3), pages 535-554, December.
    5. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    6. James D. Hamilton & Baldev Raj, 2002. "New directions in business cycle research and financial analysis," Empirical Economics, Springer, vol. 27(2), pages 149-162.
    7. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    8. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-328, April.
    9. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, July.
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    Cited by:

    1. repec:agr:journl:v:3(604):y:2015:i:3(604):p:21-38 is not listed on IDEAS
    2. Firouz Fallahi & Hamed Pourtaghi & Gabriel Rodríguez, 2012. "The unemployment rate, unemployment volatility, and crime," International Journal of Social Economics, Emerald Group Publishing, vol. 39(6), pages 440-448, May.
    3. Muhsin Kar & Tayfur Bayat & Selim Kayhan, 2016. "Impacts of Credit Default Swaps on Volatility of the Exchange Rate in Turkey: The Case of Euro," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 4(3), pages 1-18, July.
    4. Selim KAYHAN & Muhsin KAR & Ahmet ŞAHBAZ, 2015. "Is CPI a suitable tool for inflation targeting? A critical view," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(3(604), A), pages 21-38, Autumn.

    More about this item


    Markov-Switching Models; Cycles; Asymmetries; Unemployment; Crime.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes


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