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Forecasting Longevity Gains Using a Seemingly Unrelated Time Series Model
Author(s) -
Neves César,
Fernandes Cristiano,
Veiga Álvaro
Publication year - 2015
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2362
Subject(s) - unobservable , series (stratigraphy) , kalman filter , multivariate statistics , econometrics , statistics , mathematics , longevity , state space representation , sample (material) , time series , state space , algorithm , biology , paleontology , genetics , chemistry , chromatography
In this paper a multivariate time series model using the seemingly unrelated time series equation (SUTSE) framework is proposed to forecast longevity gains. The proposed model is represented in state space form and uses Kalman filtering to estimate the unobservable components and fixed parameters. We apply the model both to male mortality rates in Portugal and the USA. Our results compare favorably, in terms of mean absolute percentage error, in‐sample and out‐of‐sample, to those obtained by the Lee–Carter method and some of its extensions. Copyright © 2015 John Wiley & Sons, Ltd.