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 MORE SOPHISTICATED AND POWERFUL TECHNIQUE: VECTOR ARMA INTRODUCTION OF MULTIPLE PREDICTOR SERIES BETTER MODELS LEAD TO BETTER FORECASTS

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

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

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

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

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

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

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

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

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