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A Bayesian Reversible Jump Piecewise Hazard approach for modeling rate changes in mass shootings

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  • Andrew G. Chapple

Abstract

Time to event data for econometric tragedies, like mass shootings, have largely been ignored from a changepoint analysis standpoint. We outline a techniqnique for modeling economic changepoint problems using a piece- wise constant hazard model to explain different economic phonomenon. Specifically, we investigate the rates of mass shootings in the United States since August 20th 1982 as a case study to examine changes in rates of these terrible events in an attempt to connect changes to the shooter’s covariates or policy and societal changes.

Suggested Citation

  • Andrew G. Chapple, 2016. "A Bayesian Reversible Jump Piecewise Hazard approach for modeling rate changes in mass shootings," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 59(3), pages 19-31.
    • Handle: RePEc:eei:journl:v:59:y:2016:i:3:p:19-31
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    1. Kyu Ha Lee & Sebastien Haneuse & Deborah Schrag & Francesca Dominici, 2015. "Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(2), pages 253-273, February.
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    Cited by:

    1. Andrew G. Chapple, 2018. "Modeling ISIL terror attacks and their fatality rates with a Bayesian reversible jump marked point process," EERI Research Paper Series EERI RP 2018/09, Economics and Econometrics Research Institute (EERI), Brussels.

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    More about this item

    Keywords

    Time-to-event Data; Bayesian Analyses; Piecewise Exponential; Reversible Jump; Mass Shooting.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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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