Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model.

The parametric model introduced by Lee and Carter in 1992 for modeling mortality rates in the USA was a seminal development in forecasting life expectancies and has been widely used since then. Different extensions of this model, using different hypotheses about the data, constraints on the parameters, and appropriate methods have led to improvements in the model's fit to historical data and the model's forecasting of the future. This paper's main objective is to evaluate if differences between models are reflected in different mortality indicators' forecasts.

Model confidence sets and forecast combination: an application to age-specific mortality.

Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model.

Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts.

Mortality, longevity and experiments with the Lee-Carter model.

The paper reviews the Lee-Carter modelling framework, illustrated with an application, and then extends the framework through the development of a wider class of generalised, parametric, non-linear models. The choice of error distribution is also generalised. These extensions permit the modelling and extrapolation of age-specific cohort effects as well as the more familiar age-specific period effects: the age-period-cohort version of the model is discussed with a worked example.