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Robust non-parametric mortality and fertility modelling and forecasting: Gaussian process regression approaches

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journal contribution
posted on 2021-03-09, 11:09 authored by Ka Kin Lam, Bo Wang
A rapid decline in mortality and fertility has become major issues in many developed countries over the past few decades. An accurate model for forecasting demographic movements is important for decision making in social welfare policies and resource budgeting among the government and many industry sectors. This article introduces a novel non-parametric approach using Gaussian process regression with a natural cubic spline mean function and a spectral mixture covariance function for mortality and fertility modelling and forecasting. Unlike most of the existing approaches in demographic modelling literature, which rely on time parameters to determine the movements of the whole mortality or fertility curve shifting from one year to another over time, we consider the mortality and fertility curves from their components of all age-specific mortality and fertility rates and assume each of them following a Gaussian process over time to fit the whole curves in a discrete but intensive style. The proposed Gaussian process regression approach shows significant improvements in terms of forecast accuracy and robustness compared to other mainstream demographic modelling approaches in the short-, mid- and long-term forecasting using the mortality and fertility data of several developed countries in the numerical examples.

History

Citation

Forecasting 2021, 3(1), 207-227; https://doi.org/10.3390/forecast3010013

Author affiliation

School of Mathematics & Actuarial Science

Version

  • VoR (Version of Record)

Published in

Forecasting

Volume

3

Issue

1

Pagination

207-227

Publisher

MDPI AG

eissn

571-9394

Acceptance date

2021-03-06

Copyright date

2021

Available date

2021-03-09

Language

en

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