Forecasting school enrollments in the 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.

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Publication Details

Journal
Journal of the Royal Statistical Society Series A (Statistics in Society)
Published
2024-10-07
DOI
https://doi.org/10.1093/jrsssa/qnae094
Primary Topic
Education Systems and Policy
Type
article
Field-Weighted Citation Impact
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article

Forecasting school enrollments in the Australian Capital Territory

Caroline Hendy, Tianyu Shen, James Raymer
Journal of the Royal Statistical Society Series A (Statistics in Society)
Education Systems and Policy
article

Forecasting school enrollments in the Australian Capital Territory

Caroline Hendy, Tianyu Shen, James Raymer
article en

Abstract

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.

Journal of the Royal Statistical Society Series A (Statistics in Society)Vol. 188(4)
Australian National University (AU)
Openalex Percentile: Top 76%
Education Systems and Policy
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