TY - GEN
T1 - Forecasting subnational demographic data using seasonal time series methods
AU - Bravo, Jorge M.
AU - Coelho, Edviges
N1 - Bravo, J. M., & Coelho, E. (2019). Forecasting subnational demographic data using seasonal time series methods. In Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao 2019: 19ª Conferencia da Associacao Portuguesa de Sistemas de Informacao, CAPSI 2019 [19th Conference of the Portuguese Association for Information Systems, CAPSI 2019], Lisboa; Portugal; 11 October 2019 through 12 October 2019. Associação Portuguesa de Sistemas de Informação.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Forecasts of monthly demographic data are a critical input in the computation of infra-annual estimates of resident population since they determine, together with international net migration, the dynamics of both the population size and its age distribution. The empirical time series of demographic data exhibits strong evidence of the presence of seasonality patterns at both national and subnational levels. In this paper, we evaluate the short-term 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 (one-year) forecasting accuracy using a backtesting time series cross-validation approach.
AB - Forecasts of monthly demographic data are a critical input in the computation of infra-annual estimates of resident population since they determine, together with international net migration, the dynamics of both the population size and its age distribution. The empirical time series of demographic data exhibits strong evidence of the presence of seasonality patterns at both national and subnational levels. In this paper, we evaluate the short-term 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 (one-year) forecasting accuracy using a backtesting time series cross-validation approach.
KW - ARIMA
KW - Backtesting
KW - Population forecasts
KW - Seasonality
KW - Time series methods
UR - http://www.scopus.com/inward/record.url?scp=85086634086&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85086634086
T3 - Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao
BT - Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao 2019
PB - Associação Portuguesa de Sistemas de Informação
T2 - 19.a Conferencia da Associacao Portuguesa de Sistemas de Informacao, CAPSI 2019 - 19th Conference of the Portuguese Association for Information Systems, CAPSI 2019
Y2 - 11 October 2019 through 12 October 2019
ER -