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Spatial beta‐convergence forecasting models: Evidence from municipal homicide rates in Colombia

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  • Felipe Santos‐Marquez

Abstract

The forecasting power of different methods is tested utilizing crime data for 1120 inland municipalities in Colombia. Using data from 2003 to 2018, five different forecasting methods are used: ETS, ARIMA, STAR, a classical beta convergence based model, and a spatial beta convergence model. First, it is shown that overall municipal crime disparities are steadily decreasing over time. This indicates that convergence and spatial effects are pivotal for the study of the dynamics of crime in Colombian municipalities. Time series cross‐validation for 4‐year ahead forecasts is implemented to assess the accuracy of all models. It is found that the STAR and the beta models have the lowest root mean squared errors. Therefore, as time goes by, space appears to play a more important role in the evolution of homicide rates. The paper concludes with some policy implications in terms of spatial effects and the mitigation of crime.

Suggested Citation

  • Felipe Santos‐Marquez, 2022. "Spatial beta‐convergence forecasting models: Evidence from municipal homicide rates in Colombia," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 294-302, March.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:2:p:294-302
    DOI: 10.1002/for.2816
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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Kenny, Charles, 2005. "Why Are We Worried About Income? Nearly Everything that Matters is Converging," World Development, Elsevier, vol. 33(1), pages 1-19, January.
    4. Shoesmith, Gary L., 2013. "Space–time autoregressive models and forecasting national, regional and state crime rates," International Journal of Forecasting, Elsevier, vol. 29(1), pages 191-201.
    5. Vicente Royuela & Gustavo Adolfo Garc�a, 2015. "Economic and Social Convergence in Colombia," Regional Studies, Taylor & Francis Journals, vol. 49(2), pages 219-239, February.
    6. Robert J. Barro, 1991. "Economic Growth in a Cross Section of Countries," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(2), pages 407-443.
    7. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    8. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
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