RT Article
T1 Using ARIMA models to predict prison populations
JF Journal of quantitative criminology
VO 2
IS 3
SP 251
OP 264
A1 Lin, Bin-Shan
A2 MacKenzie, Doris Layton
A2 Gulledge, Thomas R.
LA English
YR 1986
UL https://krimdok.uni-tuebingen.de/Record/1767143079
AB In this study a time-series model for predicting Louisiana's prison population was developed using the iterative Box-Jenkins modeling methodologyidentification, estimation, and diagnostic checking. The time-series forecasts were contrasted with results of regression models and an exponential smoothing model. The results indicate that the time-series model is the superior model as indicated by the usual measures of predictive accuracy. When compared with actual data the predictions appeared sufficiently adequate to meet the needs of the correctional system for short-term planning.
K1 Time series
K1 Box-Jenkins modeling
K1 Prediction
K1 Overcrowding
K1 Prison Population
K1 Forecasting
K1 ARIMA models
DO 10.1007/BF01066529