Academic Journal
View in EDS

Forecasting school enrollments in the Australian Capital Territory.

Bibliographic Details
Title: Forecasting school enrollments in the Australian Capital Territory.
Authors: Shen, Tianyu1 (AUTHOR), Raymer, James1 (AUTHOR), Hendy, Caroline2 (AUTHOR)
Source: Journal of the Royal Statistical Society: Series A (Statistics in Society). Oct2025, Vol. 188 Issue 4, p1107-1124. 18p.
Subject Terms: *Forecasting, *Monte Carlo method, School enrollment, Population forecasting, Internal migration, Confidence intervals
Geographic Terms: Australian Capital Territory
Abstract: School enrollment forecasts are vital for effective planning. This study introduces a probabilistic multiregional population projection model, which accounts for different components, including preschool entries, migration, grade progression, and graduations. Using different distributions with a cohort component projection and Monte Carlo simulation, this paper forecasts student enrollments for each school and academic year level in the Australian Capital Territory based on the annual record-level administrative data. In the in-sample validation tests, the model's overall performance is robust, and the probabilistic design offers reliable prediction intervals reflecting the variation in observed values. The paper ends with a discussion on the importance of prediction intervals for informing school planning. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Royal Statistical Society: Series A (Statistics in Society) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Business Source Index
FullText Links:
  – Type: other
Text:
  Availability: 0
CustomLinks:
  – Url: http://www.jstor.org/openurl?issn=09641998&date=2025&volume=188&issue=4&spage=1107
    Name: JSTOR (all content) (s7799221)
    Category: fullText
    Text:
    Icon: https://imageserver.ebscohost.com/branding/jstor/iconJSTOR.gif
    MouseOverText: Full Text from JSTOR
Header DbId: bsx
DbLabel: Business Source Index
An: 188620544
RelevancyScore: 1371
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1371.22045898438
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Forecasting school enrollments in the Australian Capital Territory.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shen%2C+Tianyu%22">Shen, Tianyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Raymer%2C+James%22">Raymer, James</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hendy%2C+Caroline%22">Hendy, Caroline</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Royal+Statistical+Society%3A+Series+A+%28Statistics+in+Society%29%22">Journal of the Royal Statistical Society: Series A (Statistics in Society)</searchLink>. Oct2025, Vol. 188 Issue 4, p1107-1124. 18p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22School+enrollment%22">School enrollment</searchLink><br /><searchLink fieldCode="DE" term="%22Population+forecasting%22">Population forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Internal+migration%22">Internal migration</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Australian+Capital+Territory%22">Australian Capital Territory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: School enrollment forecasts are vital for effective planning. This study introduces a probabilistic multiregional population projection model, which accounts for different components, including preschool entries, migration, grade progression, and graduations. Using different distributions with a cohort component projection and Monte Carlo simulation, this paper forecasts student enrollments for each school and academic year level in the Australian Capital Territory based on the annual record-level administrative data. In the in-sample validation tests, the model's overall performance is robust, and the probabilistic design offers reliable prediction intervals reflecting the variation in observed values. The paper ends with a discussion on the importance of prediction intervals for informing school planning. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the Royal Statistical Society: Series A (Statistics in Society) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsx&AN=188620544
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1093/jrsssa/qnae094
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 1107
    Subjects:
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: School enrollment
        Type: general
      – SubjectFull: Population forecasting
        Type: general
      – SubjectFull: Internal migration
        Type: general
      – SubjectFull: Confidence intervals
        Type: general
      – SubjectFull: Australian Capital Territory
        Type: general
    Titles:
      – TitleFull: Forecasting school enrollments in the Australian Capital Territory.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Shen, Tianyu
      – PersonEntity:
          Name:
            NameFull: Raymer, James
      – PersonEntity:
          Name:
            NameFull: Hendy, Caroline
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 09641998
          Numbering:
            – Type: volume
              Value: 188
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Journal of the Royal Statistical Society: Series A (Statistics in Society)
              Type: main
ResultId 1