Stochastic Forecasting of Regional Age-Specific Fertility Rates: An Outlook for German NUTS-3 Regions

Patrizio Vanella () and Max J. Hassenstein
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Patrizio Vanella: Demography Cluster, Department of Health Monitoring & Biometrics, aQua Institute, 37073 Göttingen, Germany
Max J. Hassenstein: Demography Cluster, Department of Health Monitoring & Biometrics, aQua Institute, 37073 Göttingen, Germany

Mathematics, 2023, vol. 12, issue 1, 1-19

Abstract: Regional fertility forecasts are important for long-term planning in a variety of fields that include future birth numbers in their forecast, such as school or kindergarten planning. They are one of the major components of regional population forecasts as well. Therefore, it is important to construct reliable forecasts that are based on sophisticated models that cover the high complexity of future regional fertility. We suggest a novel forecast model for forecasting regional age-specific fertility rates that covers long-term trends by time series models, demographic and regional correlations by principal component analysis, and future uncertainty by Monte Carlo simulation. The model is applied to all German NUTS-3 regions (districts/Kreise) simultaneously, where we forecast all regional age-specific fertility rates through the period of 2022–2045. The results from the simulations are presented via median predictions with 75% prediction intervals of the regional total fertility rates. The simulation shows strong regional heterogeneities in long-term fertility trends that are associated with the historical background of Germany, housing supply for families, opportunities for education, and the strength of labor markets, inter alia.

Keywords: demography; regional fertility; cross-correlation; autocorrelation; time series analysis; principal component analysis; ARIMA models; forecasting; Monte Carlo simulation; stochastics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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