Forecasting mortality with international linkages: A global vector-autoregression approach

Hong Li, Yanlin Shi

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a Global Vector Autoregression (GVAR) mortality model to simultaneously model and forecast multi-population mortality dynamics. The proposed GVAR model decomposes the global regression model into population-wise local systems. Each local system consists of an intra-population autoregressive component and a small set of global factors, which contain systematic mortality information of all populations. Such a decomposition substantially reduces the extra estimation cost of including new populations compared to unconstrained VAR models, and makes the GVAR model an efficient tool for analyzing the joint mortality dynamics of a large group of populations. Further, under fairly general assumptions, the proposed GVAR model could generate coherent mortality projections between any two ages in any two populations. Using single-age mortality data of 15 low-mortality countries, we find that the global factors have substantial explanatory and forecasting power of mortality changes of individual populations, and the proposed GVAR model could produce satisfying mortality forecasts under various settings.
Original languageEnglish
Pages (from-to)59-75
Number of pages17
JournalInsurance: Mathematics and Economics
Volume100
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Global vector-autoregression
  • Coherent mortality forecasting
  • Multiple populations
  • Co-integration
  • Hyperbolic memory process

Fingerprint Dive into the research topics of 'Forecasting mortality with international linkages: A global vector-autoregression approach'. Together they form a unique fingerprint.

Cite this