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Modeling mortality with a Bayesian vector autoregression

Carolyn Ndigwako Njenga and Michael Sherris

Insurance: Mathematics and Economics, 2020, vol. 94, issue C, 40-57

Abstract: Parametric mortality models capture the cross section of mortality rates. These models fit the older ages better, because of the more complex cross section of mortality at younger and middle ages. Dynamic parametric mortality models fit a time series to the parameters, such as a Vector-auto-regression (VAR), in order to capture trends and uncertainty in mortality improvements. We consider the full age range using the Heligman and Pollard (1980) model, a cross-sectional mortality model with parameters that capture specific features of different age ranges. We make the Heligman–Pollard model dynamic using a Bayesian Vector Autoregressive (BVAR) model for the parameters and compare with more commonly used VAR models. We fit the models using Australian data, a country with similar mortality experience to many developed countries. We show how the Bayesian Vector Autoregressive (BVAR) models improve forecast accuracy compared to VAR models and quantify parameter risk which is shown to be significant.

Keywords: Mortality; Parameter risk; Vector auto-regression; Bayesian vector auto-regression; Heligman–Pollard model (search for similar items in EconPapers)
JEL-codes: C11 G22 J11 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:94:y:2020:i:c:p:40-57

DOI: 10.1016/j.insmatheco.2020.05.011

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Insurance: Mathematics and Economics is currently edited by R. Kaas, Hansjoerg Albrecher, M. J. Goovaerts and E. S. W. Shiu

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