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Forecasting Australian fertility by age, region, and birthplace

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  • Yang, Yang
  • Shang, Han Lin
  • Raymer, James

Abstract

Fertility differentials by urban–rural residence and nativity of women in Australia significantly impact population composition at sub-national levels. We aim to provide consistent fertility forecasts for Australian women characterized by age, region, and birthplace. Age-specific fertility rates at the national and sub-national levels obtained from census data between 1981 and 2011 are jointly modeled and forecast by the grouped functional time series method. Forecasts for women of each region and birthplace are reconciled following the chosen hierarchies to ensure that results at various disaggregation levels consistently sum up to the respective national total. Coupling the region of residence disaggregation structure with the trace minimization reconciliation method produces the most accurate point and interval forecasts. In addition, age-specific fertility rates disaggregated by the birthplace of women show significant heterogeneity that supports the application of the grouped forecasting method.

Suggested Citation

  • Yang, Yang & Shang, Han Lin & Raymer, James, 2024. "Forecasting Australian fertility by age, region, and birthplace," International Journal of Forecasting, Elsevier, vol. 40(2), pages 532-548.
  • Handle: RePEc:eee:intfor:v:40:y:2024:i:2:p:532-548
    DOI: 10.1016/j.ijforecast.2022.08.001
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    as
    1. Kourentzes, Nikolaos & Athanasopoulos, George, 2019. "Cross-temporal coherent forecasts for Australian tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 393-409.
    2. Han Lin Shang, 2012. "Point and interval forecasts of age-specific fertility rates: a comparison of functional principal component methods," Monash Econometrics and Business Statistics Working Papers 10/12, Monash University, Department of Econometrics and Business Statistics.
    3. Peter Hall & Céline Vial, 2006. "Assessing the finite dimensionality of functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(4), pages 689-705, September.
    4. Jeon, Jooyoung & Panagiotelis, Anastasios & Petropoulos, Fotios, 2019. "Probabilistic forecast reconciliation with applications to wind power and electric load," European Journal of Operational Research, Elsevier, vol. 279(2), pages 364-379.
    5. Stefan Rayer & Stanley Smith & Jeff Tayman, 2009. "Empirical Prediction Intervals for County Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 28(6), pages 773-793, December.
    6. Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, vol. 8(2), pages 233-241, October.
    7. Shanika L. Wickramasuriya & George Athanasopoulos & Rob J. Hyndman, 2019. "Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 804-819, April.
    8. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
    9. Zellner, Arnold & Tobias, Justin, 1998. "A Note on Aggregation, Disaggregation and Forecasting Performance," CUDARE Working Papers 198677, University of California, Berkeley, Department of Agricultural and Resource Economics.
    10. Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
    11. Eleonora Mussino & Salvatore Strozza, 2012. "The fertility of immigrants after arrival: The Italian case," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(4), pages 99-130.
    12. Liebl, Dominik, 2013. "Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective," MPRA Paper 50881, University Library of Munich, Germany.
    13. Hyndman, Rob J. & Lee, Alan J. & Wang, Earo, 2016. "Fast computation of reconciled forecasts for hierarchical and grouped time series," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 16-32.
    14. Nystrup, Peter & Lindström, Erik & Pinson, Pierre & Madsen, Henrik, 2020. "Temporal hierarchies with autocorrelation for load forecasting," European Journal of Operational Research, Elsevier, vol. 280(3), pages 876-888.
    15. Ralph Lattimore & Clinton Pobke, 2008. "Recent Trends in Australian Fertility," Staff Working Papers 0806, Productivity Commission, Government of Australia.
    16. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    17. Gregory Rice & Han Lin Shang, 2017. "A Plug-in Bandwidth Selection Procedure for Long-Run Covariance Estimation with Stationary Functional Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(4), pages 591-609, July.
    18. Robert Eastwood & Michael Lipton, 1999. "The impact of changes in human fertility on poverty," Journal of Development Studies, Taylor & Francis Journals, vol. 36(1), pages 1-30.
    19. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    20. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    21. Han Lin Shang & Yang Yang, 2021. "Forecasting Australian subnational age-specific mortality rates," Journal of Population Research, Springer, vol. 38(1), pages 1-24, March.
    22. Tom Wilson & Peter McDonald & Jeromey Temple, 2020. "The geographical patterns of birth seasonality in Australia," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(40), pages 1185-1198.
    23. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    24. Bernard Baffour & James Raymer & Ann Evans, 2021. "Correction to: Recent Trends in Immigrant Fertility in Australia," Journal of International Migration and Integration, Springer, vol. 22(3), pages 1205-1205, September.
    25. Shang, Han Lin & Kearney, Fearghal, 2022. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.
    26. Alexander Aue & Diogo Dubart Norinho & Siegfried Hörmann, 2015. "On the Prediction of Stationary Functional Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 378-392, March.
    27. Siegfried Hörmann & Łukasz Kidziński & Marc Hallin, 2015. "Dynamic functional principal components," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 319-348, March.
    28. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    29. James Robards & Ann Berrington, 2016. "The fertility of recent migrants to England and Wales," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(36), pages 1037-1052.
    30. Tom Wilson, 2017. "Comparing alternative statistics on recent fertility trends in Australia," Journal of Population Research, Springer, vol. 34(2), pages 119-133, June.
    31. Li, Han & Tang, Qihe, 2019. "Analyzing Mortality Bond Indexes Via Hierarchical Forecast Reconciliation," ASTIN Bulletin, Cambridge University Press, vol. 49(3), pages 823-846, September.
    32. James Raymer & Bernard Baffour, 2018. "Subsequent Migration of Immigrants Within Australia, 1981–2016," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(6), pages 1053-1077, December.
    33. James Raymer & Yanlin Shi & Qing Guan & Bernard Baffour & Tom Wilson, 2018. "The Sources and Diversity of Immigrant Population Change in Australia, 1981–2011," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1777-1802, October.
    34. Máire Ní Bhrolcháin & Éva Beaujouan, 2012. "Fertility postponement is largely due to rising educational enrolment," Population Studies, Taylor & Francis Journals, vol. 66(3), pages 311-327.
    35. Nico Keilman & Dinh Quang Pham, 2000. "Predictive Intervals for Age-Specific Fertility," European Journal of Population, Springer;European Association for Population Studies, vol. 16(1), pages 41-65, March.
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