Several approaches have been developed for forecasting mortality using stochastic model. In particular, the Lee Carter model (1992) has become widely used and there have been various extensions and modifications proposed to attain a broader interpretation and to capture the main features of the dynamics of the mortality intensity. Hyndman and Ullah (2005). introduce a particular version of the Lee Carter methodology, the so-called Functional Demographic Model - FDM, the most accurate approach as regards some mortality data, particularly for longer forecast horizons where the benefit of a damped trend forecast is greater. The paper objective is properly to single out the most suitable model between the basic Lee Carter and the FDM to the Italian mortality data. A comparative assessment is made. Moreover, we provide information on the uncertainty affecting the forecasted quantities by using bootstrap technique. The empirical results are presented using a range of graphical analyses.

Intensive Computational Forecasting Approach to the Functional Demographic Lee Carter Model

RUSSOLILLO, Maria;D'AMATO, VALERIA;
2009-01-01

Abstract

Several approaches have been developed for forecasting mortality using stochastic model. In particular, the Lee Carter model (1992) has become widely used and there have been various extensions and modifications proposed to attain a broader interpretation and to capture the main features of the dynamics of the mortality intensity. Hyndman and Ullah (2005). introduce a particular version of the Lee Carter methodology, the so-called Functional Demographic Model - FDM, the most accurate approach as regards some mortality data, particularly for longer forecast horizons where the benefit of a damped trend forecast is greater. The paper objective is properly to single out the most suitable model between the basic Lee Carter and the FDM to the Italian mortality data. A comparative assessment is made. Moreover, we provide information on the uncertainty affecting the forecasted quantities by using bootstrap technique. The empirical results are presented using a range of graphical analyses.
2009
9781607500728
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/2282822
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