Synergy in fertility forecasting

improving forecast accuracy through model averaging

Hanlin Shang, Heather Booth*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Accuracy in fertility forecasting has proved challenging and warrants renewed attention. One way to improve accuracy is to combine the strengths of a set of existing models through model averaging. The model-averaged forecast is derived using empirical model weights that optimise forecast accuracy at each forecast horizon based on historical data. We apply model averaging to fertility forecasting for the first time, using data for 17 countries and six models. Four model-averaging methods are compared: frequentist, Bayesian, model confidence set, and equal weights. We compute individual-model and model-averaged point and interval forecasts at horizons of one to 20 years. We demonstrate gains in average accuracy of 4–23% for point forecasts and 3–24% for interval forecasts, with greater gains from the frequentist and equal weights approaches at longer horizons. Data for England and Wales are used to illustrate model averaging in forecasting age-specific fertility to 2036. The advantages and further potential of model averaging for fertility forecasting are discussed. As the accuracy of model-averaged forecasts depends on the accuracy of the individual models, there is ongoing need to develop better models of fertility for use in forecasting and model averaging. We conclude that model averaging holds considerable promise for the improvement of fertility forecasting in a systematic way using existing models and warrants further investigation.
Original languageEnglish
Article number27
Pages (from-to)1-27
Number of pages23
JournalGenus
Volume76
DOIs
Publication statusPublished - Sep 2020

Bibliographical note

Copyright © The Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Fertility forecasting
  • Age-specific fertility
  • Forecast accuracy
  • Model averaging
  • Frequentist
  • Bayesian
  • Model confidence set
  • Computational methods
  • Functional time-series model
  • Univariate time-series model

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