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Forecasting Subnational Monthly Births and Deaths using Seasonal Time Series Methods
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Orientador(es)
Resumo(s)
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.
Descrição
Bravo, J. M., & Coelho, E. (2019). Forecasting Subnational Monthly Births and Deaths using Seasonal Time Series Methods. In Evidence-based territorial policymaking: formulation, implementation and evaluation of policy: 26th APDR Congress Proceedings (pp. 1079-1088). Associacao Portuguesa para o Desenvolvimento Regional (APDR).
Palavras-chave
Holt-Winters method Time series methods Seasonality State-Space models SARIMA Population forecasting
Contexto Educativo
Citação
Editora
APDR - Associação Portuguesa para o Desenvolvimento Regional