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TRENDS IN YOUTH HOMICIDE: A MULTIVARIATE ASSESSMENT AND FORECASTING FOR POLICY IMPACT

TRENDS IN YOUTH HOMICIDE: A MULTIVARIATE ASSESSMENT AND FORECASTING FOR POLICY IMPACT. ROBERT NASH PARKER UNIVERSITY OF CALIFORNIA EMILY K. ASENCIO UNIVERSITY OF AKRON. TIME SERIES AS POLICY TOOL. IMPROVING TREND ANALYSIS LEADS TO: BETTER PREDICTIVE MODELS BASED ON

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TRENDS IN YOUTH HOMICIDE: A MULTIVARIATE ASSESSMENT AND FORECASTING FOR POLICY IMPACT

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  1. TRENDS IN YOUTHHOMICIDE: A MULTIVARIATE ASSESSMENT AND FORECASTING FOR POLICY IMPACT ROBERT NASH PARKER UNIVERSITY OF CALIFORNIA EMILY K. ASENCIO UNIVERSITY OF AKRON

  2. TIME SERIES AS POLICY TOOL • IMPROVING TREND ANALYSIS LEADS TO: • BETTER PREDICTIVE MODELS BASED ON • MORE SOPHISTICATED AND POWERFUL TECHNIQUE: VECTOR ARMA • INTRODUCTION OF MULTIPLE PREDICTOR SERIES • BETTER MODELS LEAD TO BETTER FORECASTS

  3. FORECASTING AS POLICY TOOL? • FORECASTING CAN BE A POLICY TOOL • IF FORECASTS ARE GOOD ENOUGH, CITIES COULD BE COMPARED TO THE FORECAST • IF OUTSIDE THE CONFIDENCE INTERVALS ON THE UP SIDE, CALL FOR INTERVENTIONS • IF OUTSIDE THE CONFIDENCE INTERVALS ON THE DOWN SIDE, MODEL TO BE STUDIED

  4. MULTIVARIATE ASSESSMENT • TWO MEANINGS • USING VECTOR ARMA APPROACH TO SIMULTANEOULSY ANALYZE THE SIGNAL TO NOISE STRUCTURE OF TRENDS IN ALL 91 CITIES • INTRODUCE PREDICTOR TIME SERIES TO IMPROVE MODELS AND FORECASTING • PRELIMINARY RESULTS PRESENTED ON THE FIRST POINT

  5. VECTOR ARMA • Advanced development of time series methodology by G.E.P. Box, one of two developers of ARIMA modeling (Tiao and Box, 1981 JASA) • More general model that subsumes ARIMA and transfer function models into a powerful general multivariate model • Several advantages over ARIMA and transfer function

  6. VECTOR ARMA • Advantages include: • Common model derived for all times series analyzed, dependent or independent • Reciprocal relationships and feedback equations can be specified • Modeling of signal and noise components part of structural model, not forced into the error term • Overall structural model is usually simpler

  7. Results • Modeled the three time series for 91 cities simultaneously • Present model results and predictions/forecasting for each set of series

  8. Vector ARMA Model Results • Procedures similar to ARIMA • Estimate models, examine residual matrices, adjust and re-estimate • AR, MA and differencing, plus transformations can be examined • AR imply one shot impact; MA are averaged impacts • AR,MA weighted; Difference implies simple unweighted relationship by lag length

  9. Results • Aged 13-17: • AR Lag1: .65 (SE: .15); MA Lag1: .32 (SE: .21) • 0 difference; significant constant • Aged 18-24: • AR lag1: .59 (SE: .25); MA Lag1: .33 (SE: .21) • 0 difference; significant constant • Aged 25+: • AR lag1: .65 (SE: .25); MA Lag1: .50 (SE: .31) • 0 difference; significant constant

  10. Prediction and Forecasting results • Plotted with upper and lower 66% and 95% confidence intervals

  11. Policy tools and impact • Assess your city based on these structural models and forecasts • Results could suggest interventions or the ability to focus resources elsewhere • These models and forecasts should be improved by introducing predictor series • Should be routinely updated and provided to city leaders and law enforcement

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