Forecasting small population monthly fertility and mortality data with seasonal time series methods

Bibliographic Details
Main Author: Bravo, Jorge Miguel
Publication Date: 2020
Other Authors: Coelho, Edviges Isabel Felizardo
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/91961
Summary: Bravo, J. M., & Coelho, E. I. F. (2020). Forecasting small population monthly fertility and mortality data with seasonal time series methods. In W. L. Linhares (Ed.), As Ciências Sociais Aplicadas e a Interface com vários Saberes (Vol. 2, pp. 158-176). Atena. https://doi.org/10.22533/at.ed.79020280112
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Forecasting small population monthly fertility and mortality data with seasonal time series methodsSmall population forecastsSARIMABacktestingseasonalityState Space modelsSDG 3 - Good Health and Well-beingSDG 11 - Sustainable Cities and CommunitiesBravo, J. M., & Coelho, E. I. F. (2020). Forecasting small population monthly fertility and mortality data with seasonal time series methods. In W. L. Linhares (Ed.), As Ciências Sociais Aplicadas e a Interface com vários Saberes (Vol. 2, pp. 158-176). Atena. https://doi.org/10.22533/at.ed.79020280112Forecasts of small population monthly fertility and mortality data are a critical input in the computation of subnational forecasts of resident population since they determine, together with internal and international net migration, the dynamics of both the population size and its age structure. Demographic time series data typically exhibit strong seasonality patterns at both national and regional levels. In this paper, we evaluate the short-term forecasting accuracy 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 local and regional level. We adopt a backtesting time series cross-validation approach considering a multi-step forecasting approach with re-estimation. Additionally, we investigate the model’s performance in terms of forecasting uncertainty by computing the percentage of actual monthly births and death counts which fall out of prediction intervals. We use a time series of monthly birth and death data for the 25 Portuguese NUTS3 regions from 2000 to 2018, disaggregated by sex.AtenaNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBravo, Jorge MiguelCoelho, Edviges Isabel Felizardo2020-01-30T23:16:50Z2020-012020-01-01T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersion18application/pdfhttp://hdl.handle.net/10362/91961eng978-85-7247-979-0PURE: 15570109https://doi.org/10.22533/at.ed.79020280112info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:43:13Zoai:run.unl.pt:10362/91961Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:14:36.365177Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Forecasting small population monthly fertility and mortality data with seasonal time series methods
title Forecasting small population monthly fertility and mortality data with seasonal time series methods
spellingShingle Forecasting small population monthly fertility and mortality data with seasonal time series methods
Bravo, Jorge Miguel
Small population forecasts
SARIMA
Backtesting
seasonality
State Space models
SDG 3 - Good Health and Well-being
SDG 11 - Sustainable Cities and Communities
title_short Forecasting small population monthly fertility and mortality data with seasonal time series methods
title_full Forecasting small population monthly fertility and mortality data with seasonal time series methods
title_fullStr Forecasting small population monthly fertility and mortality data with seasonal time series methods
title_full_unstemmed Forecasting small population monthly fertility and mortality data with seasonal time series methods
title_sort Forecasting small population monthly fertility and mortality data with seasonal time series methods
author Bravo, Jorge Miguel
author_facet Bravo, Jorge Miguel
Coelho, Edviges Isabel Felizardo
author_role author
author2 Coelho, Edviges Isabel Felizardo
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Bravo, Jorge Miguel
Coelho, Edviges Isabel Felizardo
dc.subject.por.fl_str_mv Small population forecasts
SARIMA
Backtesting
seasonality
State Space models
SDG 3 - Good Health and Well-being
SDG 11 - Sustainable Cities and Communities
topic Small population forecasts
SARIMA
Backtesting
seasonality
State Space models
SDG 3 - Good Health and Well-being
SDG 11 - Sustainable Cities and Communities
description Bravo, J. M., & Coelho, E. I. F. (2020). Forecasting small population monthly fertility and mortality data with seasonal time series methods. In W. L. Linhares (Ed.), As Ciências Sociais Aplicadas e a Interface com vários Saberes (Vol. 2, pp. 158-176). Atena. https://doi.org/10.22533/at.ed.79020280112
publishDate 2020
dc.date.none.fl_str_mv 2020-01-30T23:16:50Z
2020-01
2020-01-01T00:00:00Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/91961
url http://hdl.handle.net/10362/91961
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-85-7247-979-0
PURE: 15570109
https://doi.org/10.22533/at.ed.79020280112
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 18
application/pdf
dc.publisher.none.fl_str_mv Atena
publisher.none.fl_str_mv Atena
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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