MEM_Manuscript/README.md

In mpascariu/MortalityForecast: Standard Tools to Compare and Evaluate Various Mortality Forecasting Methods

The Maximum Entropy Mortality Model: Forecasting mortality using statistical moments

This repository contains the source code (R & Tex) used in performing the analysis, writing and compiling the manuscript.

Authors

Marius D. Pascariu, Adam Lenart & Vladimir Canudas-Romo

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.

Link to the published article:

https://doi.org/10.1080/03461238.2019.1596974

To cite this article:

Marius D. Pascariu, Adam Lenart & Vladimir Canudas-Romo (2019): The maximum entropy mortality model: forecasting mortality using statistical moments, Scandinavian Actuarial Journal

mpascariu/MortalityForecast documentation built on Sept. 28, 2020, 2:40 p.m.