Using ARIMA models to predict prison populations

Bin Shan Lin, Doris Layton MacKenzie, Thomas R. Gulledge

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)251-264
Number of pages14
JournalJournal of Quantitative Criminology
Volume2
Issue number3
DOIs
StatePublished - Sep 1 1986

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Law

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