Forecasting Longevity Gains Using a Seemingly Unrelated Time Series Model

Abstract

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.