Forecasting Subnational Monthly Births and Deaths using Seasonal Time Series Methods

Jorge Miguel Bravo, Edviges Coelho

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Forecasts of monthly births and deaths are a critical input in the computation of monthly estimates of resident population since they determine, together with international net migration, the dynamics of both the population size and
its age distribution. Empirical time series data for births and deaths exhibits strong evidence of the presence of seasonality patterns at both national and subnational levels. In this paper, we evaluate the forecasting performance of alternative linear and non-linear time series methods (seasonal ARIMA, Holt-Winters and State Space models) to birth and death monthly forecasting at the sub-national level. Additionally, we investigate how well the models perform in terms of predicting the uncertainty of future monthly birth and death counts. We use the series of monthly birth and death data from 2000 to 2018 disaggregated by sex for the 25 Portuguese NUTS3 regions to compare the model's short-term (oneyear) forecasting accuracy using a backtesting time series cross- validation approach. Our results provide valuable insights regarding the forecasting performance of alternative time series models in small population forecasting exercises and on the validity of using such models as predictors of population forecast uncertainty.
Original languageEnglish
Title of host publicationEvidence-based territorial policymaking: formulation, implementation and evaluation of policy
Subtitle of host publication26th APDR Congress Proceedings
PublisherAssociacao Portuguesa para o Desenvolvimento Regional (APDR)
Number of pages10
ISBN (Electronic)978-989-8780-07-2
Publication statusPublished - Jul 2019
Event26th APDR Congress: Evidence-based territorial policymaking - Universidade de Aveiro, Aveiro, Portugal
Duration: 4 Jul 20195 Jul 2019
Conference number: 26


Conference26th APDR Congress
Abbreviated titleAPDR
Internet address


  • Holt-Winters method
  • Time series methods
  • Seasonality
  • State-Space models
  • Population forecasting


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