Forecasting the Retirement Age
| Main Author: | |
|---|---|
| Publication Date: | 2021 |
| Other Authors: | |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10362/118589 |
Summary: | ravo, J. M., & Ayuso, M. (2021). Forecasting the Retirement Age: A Bayesian Model Ensemble Approach. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & A. M. R. Correia (Eds.), Trends and Applications in Information Systems and Technologies (pp. 123-135). (Advances in Intelligent Systems and Computing; Vol. 1365 AIST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72657-7_12 |
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Forecasting the Retirement AgeA Bayesian Model Ensemble ApproachBayesian Model EnsembleLife expectancy gapMortality forecastingPension design and policyRetirement ageStochastic methodsControl and Systems EngineeringComputer Science(all)SDG 3 - Good Health and Well-beingravo, J. M., & Ayuso, M. (2021). Forecasting the Retirement Age: A Bayesian Model Ensemble Approach. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & A. M. R. Correia (Eds.), Trends and Applications in Information Systems and Technologies (pp. 123-135). (Advances in Intelligent Systems and Computing; Vol. 1365 AIST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72657-7_12In recent decades, most countries have responded to continuous longevity improvements and population ageing with pension reforms. Increasing early and normal retirement ages in an automatic or scheduled way as life expectancy at old age progresses has been one of the most common policy responses of public and private pension schemes. This paper provides comparable cross-country forecasts of the retirement age for public pension schemes for selected countries that introduced automatic indexation of pension ages to life expectancy pursuing alternative retirement age policies and goals. We use a Bayesian Model Ensemble of heterogeneous parametric models, principal component methods, and smoothing approaches involving both the selection of the model confidence set and the determination of optimal weights based on model’s forecasting accuracy. Model-averaged Bayesian credible prediction intervals are derived accounting for both stochastic process, model, and parameter risks. Our results show that statutory retirement ages are forecasted to increase substantially in the next decades, particularly in countries that have opted to target a constant period in retirement. The use of cohort and not period life expectancy measures in pension age indexation formulas would raise retirement ages even further. These results have important micro and macroeconomic implications for the design of pension schemes and individual lifecycle planning.Springer Science and Business Media Deutschland GmbHInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNBravo, Jorge M.Ayuso, Mercedes2023-03-12T01:32:31Z2021-04-232021-04-23T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion13application/pdfhttp://hdl.handle.net/10362/118589eng97830307265602194-5357PURE: 31609272https://doi.org/10.1007/978-3-030-72657-7_12info: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:RCAAP2024-05-22T17:53:35Zoai:run.unl.pt:10362/118589Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:24:48.476984Repositórios 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 the Retirement Age A Bayesian Model Ensemble Approach |
| title |
Forecasting the Retirement Age |
| spellingShingle |
Forecasting the Retirement Age Bravo, Jorge M. Bayesian Model Ensemble Life expectancy gap Mortality forecasting Pension design and policy Retirement age Stochastic methods Control and Systems Engineering Computer Science(all) SDG 3 - Good Health and Well-being |
| title_short |
Forecasting the Retirement Age |
| title_full |
Forecasting the Retirement Age |
| title_fullStr |
Forecasting the Retirement Age |
| title_full_unstemmed |
Forecasting the Retirement Age |
| title_sort |
Forecasting the Retirement Age |
| author |
Bravo, Jorge M. |
| author_facet |
Bravo, Jorge M. Ayuso, Mercedes |
| author_role |
author |
| author2 |
Ayuso, Mercedes |
| author2_role |
author |
| dc.contributor.none.fl_str_mv |
Information Management Research Center (MagIC) - NOVA Information Management School NOVA Information Management School (NOVA IMS) RUN |
| dc.contributor.author.fl_str_mv |
Bravo, Jorge M. Ayuso, Mercedes |
| dc.subject.por.fl_str_mv |
Bayesian Model Ensemble Life expectancy gap Mortality forecasting Pension design and policy Retirement age Stochastic methods Control and Systems Engineering Computer Science(all) SDG 3 - Good Health and Well-being |
| topic |
Bayesian Model Ensemble Life expectancy gap Mortality forecasting Pension design and policy Retirement age Stochastic methods Control and Systems Engineering Computer Science(all) SDG 3 - Good Health and Well-being |
| description |
ravo, J. M., & Ayuso, M. (2021). Forecasting the Retirement Age: A Bayesian Model Ensemble Approach. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & A. M. R. Correia (Eds.), Trends and Applications in Information Systems and Technologies (pp. 123-135). (Advances in Intelligent Systems and Computing; Vol. 1365 AIST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72657-7_12 |
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2021 |
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2021-04-23 2021-04-23T00:00:00Z 2023-03-12T01:32:31Z |
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conference object |
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http://hdl.handle.net/10362/118589 |
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eng |
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eng |
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9783030726560 2194-5357 PURE: 31609272 https://doi.org/10.1007/978-3-030-72657-7_12 |
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13 application/pdf |
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Springer Science and Business Media Deutschland GmbH |
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Springer Science and Business Media Deutschland GmbH |
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