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Is the recent increase in national homicide abnormal? Testing the application of fan charts in monitoring national homicide trends over time

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  • Yim, Ha-Neul

    (The University of Texas at Dallas)

  • Riddell, Jordan R.
  • Wheeler, Andrew Palmer

    (University of Texas at Dallas)

Abstract

Purpose: The goal of this study is to compare the increase in the 2015 national homicide rate to the historical data series and other violent crime rate changes. Methods: We use ARIMA models and a one-step ahead forecasting technique to predict national homicide, rape, robbery, and aggravated assault rates in the United States. Annual Uniform Crime Report data published by the Federal Bureau of Investigation are used in our analysis. Results: The 2015 homicide rate increased above the 90% prediction interval for our model, but not more conservative intervals. Predictions intervals for other national level crime rates consistently produced correct coverage using our forecasting approach. Conclusions: Our findings provide weak evidence that the national homicide rate spiked in 2015, though data for 2016 – 2018 do not show a continued anomalous increase in the U.S. homicide rate. Data and code to replicate the findings can be downloaded from https://www.dropbox.com/sh/3086vtoqly5qho6/AABq_weh2LTMtBp426vhZ0EHa?dl=0

Suggested Citation

  • Yim, Ha-Neul & Riddell, Jordan R. & Wheeler, Andrew Palmer, 2019. "Is the recent increase in national homicide abnormal? Testing the application of fan charts in monitoring national homicide trends over time," SocArXiv 7g32n, Center for Open Science.
    • Handle: RePEc:osf:socarx:7g32n
    DOI: 10.31219/osf.io/7g32n
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    1. Anne Morrison Piehl & Suzanne J. Cooper & Anthony A. Braga & David M. Kennedy, 2003. "Testing for Structural Breaks in the Evaluation of Programs," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 550-558, August.
    2. Barnett, Arnold & Kleitman, Daniel J. & Larson, Richard C., 1975. "On urban homicide: A statistical analysis," Journal of Criminal Justice, Elsevier, vol. 3(2), pages 85-110.
    3. David Cronin & Kevin Dowd, 2013. "Fiscal Fan Charts: A Tool for Assessing Member States' (Likely?) Compliance with EU Fiscal Rules," Fiscal Studies, Institute for Fiscal Studies, vol. 34, pages 517-534, December.
    4. Paret, Anne-Charlotte, 2017. "Debt sustainability in emerging market countries: Some policy guidelines from a fan-chart approach," Economic Modelling, Elsevier, vol. 63(C), pages 26-45.
    5. Matei Demetrescu & Mu-Chun Wang, 2014. "Incorporating Asymmetric Preferences into Fan Charts and Path Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 287-297, April.
    6. Martin Fukač & Robert Kirkby, 2017. "Accounting for Uncertainty in Public Debt Targets," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(1), pages 89-102, March.
    7. Kenneth F. Wallis, 1999. "Asymmetric density forecasts of inflation and the Bank of England's fan chart," National Institute Economic Review, National Institute of Economic and Social Research, vol. 167(1), pages 106-112, January.
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