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Article: Using ARIMA models to predict prison populations

TitleUsing ARIMA models to predict prison populations
Authors
Lin, B-SMacKenzie, DLGulledge Jr, TR
KeywordsArima Models
Box-Jenkins Modeling
Forecasting
Overcrowding
Prediction
Prison Population
Time Series
Issue Date1986
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0748-4518
Citation
Journal of Quantitative Criminology, 1986, v. 2 n. 3, p. 251-264 How to Cite?
DOI: http://dx.doi.org/10.1007/BF01066529
AbstractIn 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. © 1986 Plenum Publishing Corporation.
Persistent Identifierhttp://hdl.handle.net/10722/91066
ISSN
0748-4518
2023 Impact Factor: 2.8
2023 SCImago Journal Rankings: 1.367

 

DC FieldValueLanguage
dc.contributor.authorLin, B-Sen_HK
dc.contributor.authorMacKenzie, DLen_HK
dc.contributor.authorGulledge Jr, TRen_HK
dc.date.accessioned2010-09-17T10:12:31Z-
dc.date.available2010-09-17T10:12:31Z-
dc.date.issued1986en_HK
dc.identifier.citationJournal of Quantitative Criminology, 1986, v. 2 n. 3, p. 251-264en_HK
dc.identifier.issn0748-4518en_HK
dc.identifier.urihttp://hdl.handle.net/10722/91066-
dc.description.abstractIn 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. © 1986 Plenum Publishing Corporation.en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0748-4518en_HK
dc.relation.ispartofJournal of Quantitative Criminologyen_HK
dc.subjectArima Modelsen_HK
dc.subjectBox-Jenkins Modelingen_HK
dc.subjectForecastingen_HK
dc.subjectOvercrowdingen_HK
dc.subjectPredictionen_HK
dc.subjectPrison Populationen_HK
dc.subjectTime Seriesen_HK
dc.titleUsing ARIMA models to predict prison populationsen_HK
dc.typeArticleen_HK
dc.identifier.emailLin, B:blin@hku.hken_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/BF01066529en_HK
dc.identifier.scopuseid_2-s2.0-0009173947en_HK
dc.identifier.volume2en_HK
dc.identifier.issue3en_HK
dc.identifier.spage251en_HK
dc.identifier.epage264en_HK
dc.identifier.eissn1573-7799-
dc.identifier.issnl0748-4518-

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