IDEAS home Printed from https://ideas.repec.org/a/eee/jcjust/v66y2020ics0047235219304672.html
   My bibliography  Save this article

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

Author

Listed:
  • Yim, Ha-Neul
  • Riddell, Jordan R.
  • Wheeler, Andrew P.

Abstract

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.

Suggested Citation

  • Yim, Ha-Neul & Riddell, Jordan R. & Wheeler, Andrew P., 2020. "Is the recent increase in national homicide abnormal? Testing the application of fan charts in monitoring national homicide trends over time," Journal of Criminal Justice, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:jcjust:v:66:y:2020:i:c:s0047235219304672
    DOI: 10.1016/j.jcrimjus.2019.101656
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047235219304672
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jcrimjus.2019.101656?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Pyrooz, David C. & Decker, Scott H. & Wolfe, Scott E. & Shjarback, John A., 2016. "Was there a Ferguson Effect on crime rates in large U.S. cities?," Journal of Criminal Justice, Elsevier, vol. 46(C), pages 1-8.
    4. Shjarback, John A. & Pyrooz, David C. & Wolfe, Scott E. & Decker, Scott H., 2017. "De-policing and crime in the wake of Ferguson: Racialized changes in the quantity and quality of policing among Missouri police departments," Journal of Criminal Justice, Elsevier, vol. 50(C), pages 42-52.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Lang, Michael & Schmidt, Paul G., 2016. "The early warnings of banking crises: Interaction of broad liquidity and demand deposits," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 1-29.
    10. 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.
    11. 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).
    12. Kenneth W. Clements & H. Y. Izan & Yihui Lan, 2013. "Volatility and stock price indexes," Applied Economics, Taylor & Francis Journals, vol. 45(22), pages 3255-3262, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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. Tanaya Devi & Roland G. Fryer Jr, 2020. "Policing the Police: The Impact of "Pattern-or-Practice" Investigations on Crime," NBER Working Papers 27324, National Bureau of Economic Research, Inc.
    4. Nix, Justin & Pickett, Justin T., 2017. "Third-person perceptions, hostile media effects, and policing: Developing a theoretical framework for assessing the Ferguson effect," Journal of Criminal Justice, Elsevier, vol. 51(C), pages 24-33.
    5. Mourtgos, Scott M. & Adams, Ian T., 2019. "The rhetoric of de-policing: Evaluating open-ended survey responses from police officers with machine learning-based structural topic modeling," Journal of Criminal Justice, Elsevier, vol. 64(C), pages 1-1.
    6. Ratcliffe, Jerry H. & Taylor, Ralph B., 2023. "The disproportionate impact of post-George Floyd violence increases on minority neighborhoods in Philadelphia," Journal of Criminal Justice, Elsevier, vol. 88(C).
    7. Cynthia Lum & Christopher S. Koper & David B. Wilson & Megan Stoltz & Michael Goodier & Elizabeth Eggins & Angela Higginson & Lorraine Mazerolle, 2020. "Body‐worn cameras’ effects on police officers and citizen behavior: A systematic review," Campbell Systematic Reviews, John Wiley & Sons, vol. 16(3), September.
    8. Cheng, Cheng & Long, Wei, 2022. "The effect of highly publicized police killings on policing: Evidence from large U.S. cities," Journal of Public Economics, Elsevier, vol. 206(C).
    9. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    10. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    11. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 53(6), pages 286-303, January.
    12. Kemp, Gordon C.R. & Santos Silva, J.M.C., 2012. "Regression towards the mode," Journal of Econometrics, Elsevier, vol. 170(1), pages 92-101.
    13. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    14. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    15. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    16. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    17. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.
    18. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    19. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    20. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jcjust:v:66:y:2020:i:c:s0047235219304672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jcrimjus .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.