Modelling mortality: a bayesian factor-augmented var (favar) approach

Yang Lu, Dan Zhu

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

Longevity risk is putting more and more financial pressure on governments and pension plans worldwide due to pensioners' increasing trend of life expectancy and the growing numbers of people reaching retirement age. Lee and Carter (1992, Journal of the American Statistical Association, 87(419), 659-671.) applied a one-factor dynamic factor model to forecast the trend of mortality improvement, and the model has since become the field's workhorse. It is, however, well known that their model is subject to the limitation of overlooking cross-dependence between different age groups. We introduce Factor-Augmented Vector Autoregressive (FAVAR) models to the mortality modelling literature. The model, obtained by adding an unobserved factor process to a Vector Autoregressive (VAR) process, nests VAR and Lee-Carter models as special cases and inherits both frameworks' advantages. A Bayesian estimation approach, adapted from the Minnesota prior, is proposed. The empirical application to the US and French mortality data demonstrates our proposed method's efficacy in both in-sample and out-of-sample performance.

Original languageEnglish
Pages (from-to)29-61
Number of pages33
JournalASTIN Bulletin
Volume53
Issue number1
DOIs
Publication statusPublished - Jan 2023

Keywords

  • Bayesian VAR
  • Factor-augmented VAR
  • Gibbs sampler
  • longevity phenomenon
  • Minnesota prior

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