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Two‐Dimensional Kernel Smoothing of Mortality Surface: An Evaluation of Cohort Strength

Two‐Dimensional Kernel Smoothing of Mortality Surface: An Evaluation of Cohort Strength Accurate mortality forecasts are of primary interest to insurance companies, pension providers and government welfare systems owing to the rapid increase in life expectancy during the past few decades. Existing mortality models in the literature tend to project future mortality rates by extracting the observed patterns in the mortality surface. Patterns found in the cohort dimension have received a considerable amount of attention and are included in many models of mortality. However, to our knowledge very few studies have considered an evaluation and comparison of cohort patterns across different countries. Moreover, the answer to the question of how the incorporation of the cohort effect affects the forecasting performance of mortality models still remains unclear. In this paper we introduce a new way of incorporating the cohort effect at the beginning of the estimation stage via the implementation of kernel smoothing techniques. Bivariate standard normal kernel density is used and we capture the cohort effect by assigning greater weights along the diagonals of the mortality surface. Based on the results from our empirical study, we compare and discuss the differences in cohort strength across a range of developed countries. Further, the fitting and forecasting results demonstrate the superior performance of our model when compared to some well‐known mortality models in the literature under a majority of circumstances. Copyright © 2016 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Forecasting Wiley

Two‐Dimensional Kernel Smoothing of Mortality Surface: An Evaluation of Cohort Strength

Li, Han; O'hare, Colin; Vahid, Farshid
Journal of Forecasting , Volume 35 (6) – Jan 1, 2016
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References (28)

Publisher
Wiley
Copyright
Copyright © 2016 John Wiley & Sons, Ltd.
ISSN
0277-6693
eISSN
1099-131X
DOI
10.1002/for.2399
Publisher site
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Abstract

Accurate mortality forecasts are of primary interest to insurance companies, pension providers and government welfare systems owing to the rapid increase in life expectancy during the past few decades. Existing mortality models in the literature tend to project future mortality rates by extracting the observed patterns in the mortality surface. Patterns found in the cohort dimension have received a considerable amount of attention and are included in many models of mortality. However, to our knowledge very few studies have considered an evaluation and comparison of cohort patterns across different countries. Moreover, the answer to the question of how the incorporation of the cohort effect affects the forecasting performance of mortality models still remains unclear. In this paper we introduce a new way of incorporating the cohort effect at the beginning of the estimation stage via the implementation of kernel smoothing techniques. Bivariate standard normal kernel density is used and we capture the cohort effect by assigning greater weights along the diagonals of the mortality surface. Based on the results from our empirical study, we compare and discuss the differences in cohort strength across a range of developed countries. Further, the fitting and forecasting results demonstrate the superior performance of our model when compared to some well‐known mortality models in the literature under a majority of circumstances. Copyright © 2016 John Wiley & Sons, Ltd.

Journal

Journal of Forecasting – Wiley

Published: Jan 1, 2016

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