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Journal of Population Research

, Volume 35, Issue 3, pp 289–318 | Cite as

Forecasting mortality rates: multivariate or univariate models?

  • Lingbing Feng
  • Yanlin Shi
Original Research
  • 42 Downloads

Abstract

It is well known that accurate forecasts of mortality rates are essential to various demographic research topics, such as population projections and the pricing of insurance products such as pensions and annuities. In this study, we argue that including the lagged rates of neighbouring ages cannot further improve mortality forecasting after allowing for autocorrelations. This is because the sample cross-correlation function cannot exhibit meaningful and statistically significant correlations. In other words, rates of neighbouring ages are usually not leading indicators in mortality forecasting. Therefore, multivariate stochastic mortality models like the classic Lee–Carter may not necessarily lead to more accurate forecasts, compared with sophisticated univariate models. Using Australian mortality data, simulation and empirical studies employing the Lee–Carter, Functional Data, Vector Autoregression, Autoregression-Autoregressive Conditional Heteroskedasticity and exponential smoothing (ETS) state space models are performed. Results suggest that ETS models consistently outperform the others in terms of forecasting accuracy. This conclusion holds for both female and male mortality data with different empirical features across various forecasting error measurements. Hence, ETS can be a widely useful tool to model and forecast mortality rates in actuarial practice.

Keywords

Mortality rates Multivariate model Univariate model Exponential smoothing Lee–Carter model 

Notes

Acknowledgements

We are grateful to the Macquarie University and Jiangxi University of Finance and Economics for their support. The authors would also like to thank Rob Hyndman, James Raymer and Hanlin Shang for their helpful comments and suggestions. We are also grateful for the valuable advice provided by the Editor (Santosh Jatrana) and two anonymous referees. The usual disclaimer applies.

Supplementary material

12546_2018_9205_MOESM1_ESM.pdf (499 kb)
Supplementary material 1 (PDF 498 kb)

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.International Institute of Financial StudiesJiangxi University of Finance and EconomicsNanchangChina
  2. 2.Department of Actuarial Studies and Business AnalyticsMacquarie UniversitySydneyAustralia

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