The maximum entropy mortality model: forecasting mortality using statistical moments
Date
2019
Authors
Pascariu, Marius
Lenart, Adam
Canudas Romo, Vladimir
Journal Title
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Volume Title
Publisher
Taylor & Francis
Abstract
The age-at-death distribution is a representation of the mortality experience
in a population. Although it proves to be highly informative, it is often
neglected when it comes to the practice of past or future mortality assessment.
We propose an innovative method to mortality modeling and forecasting
by making use of the location and shape measures of a density
function, i.e. statistical moments. Time series methods for extrapolating a
limited number of moments are used and then the reconstruction of the
future age-at-death distribution is performed. The predictive power of the
method seems to be net superior when compared to the results obtained
using classical approaches to extrapolating age-specific-death rates, and
the accuracy of the point forecast (MASE) is improved on average by 33%
respective to the state-of-the-art, the Lee–Carter model. The method is
tested using data from the Human Mortality Database and implemented
in a publicly available R package
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Source
Scandinavian Actuarial Journal
Type
Journal article
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Open Access
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Creative Commons Attribution License
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