Literature DB >> 33603592

Mortality Forecasting with the Lee-Carter Method: Adjusting for Smoothing and Lifespan Disparity.

Ahbab Mohammad Fazle Rabbi1, Stefano Mazzuco1.   

Abstract

Reliable mortality forecasts are an essential component of healthcare policies in ageing societies. The Lee-Carter method and its later variants are widely accepted probabilistic approaches to mortality forecasting, due to their simplicity and the straightforward interpretation of the model parameters. This model assumes an invariant age component and linear time component for forecasting. We apply the Lee-Carter method on smoothed mortality rates obtained by LASSO-type regularization and hence adjust the time component with the observed lifespan disparity. Smoothing with LASSO produces less error during the fitting period than do spline-based smoothing techniques. As a more informative indicator of longevity, matching with lifespan disparity makes the time component more reflective of mortality improvements. The forecasts produced by the new method were more accurate during out-of-sample evaluation and provided optimistic forecasts for many low-mortality countries. © Springer Nature B.V. 2020.

Entities:  

Keywords:  LASSO; Lifespan disparity; Mortality forecasting; Mortality smoothing

Year:  2020        PMID: 33603592      PMCID: PMC7865054          DOI: 10.1007/s10680-020-09559-9

Source DB:  PubMed          Journal:  Eur J Popul        ISSN: 0168-6577


  19 in total

1.  Applying Lee-Carter under conditions of variable mortality decline.

Authors:  Heather Booth; John Maindonald; Len Smith
Journal:  Popul Stud (Camb)       Date:  2002-11

2.  Length of life inequality around the globe.

Authors:  Jeroen Smits; Christiaan Monden
Journal:  Soc Sci Med       Date:  2009-01-26       Impact factor: 4.634

3.  Bayesian Population Forecasting: Extending the Lee-Carter Method.

Authors:  Arkadiusz Wiśniowski; Peter W F Smith; Jakub Bijak; James Raymer; Jonathan J Forster
Journal:  Demography       Date:  2015-06

4.  Modelling and forecasting adult age-at-death distributions.

Authors:  Ugofilippo Basellini; Carlo Giovanni Camarda
Journal:  Popul Stud (Camb)       Date:  2019-01-29

5.  Long term mortality trends behind low life expectancy of Danish women.

Authors:  R Jacobsen; N Keiding; E Lynge
Journal:  J Epidemiol Community Health       Date:  2002-03       Impact factor: 3.710

6.  Bayesian probabilistic projections of life expectancy for all countries.

Authors:  Adrian E Raftery; Jennifer L Chunn; Patrick Gerland; Hana Sevčíková
Journal:  Demography       Date:  2013-06

7.  Life expectancy versus lifespan inequality: A smudge or a clear relationship?

Authors:  László Németh
Journal:  PLoS One       Date:  2017-09-28       Impact factor: 3.240

8.  Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts.

Authors:  Christina Bohk-Ewald; Marcus Ebeling; Roland Rau
Journal:  Demography       Date:  2017-08

9.  Probabilistic mortality forecasting with varying age-specific survival improvements.

Authors:  Christina Bohk-Ewald; Roland Rau
Journal:  Genus       Date:  2017-01-12

10.  Comparing strategies for matching mortality forecasts to the most recently observed data: exploring the trade-off between accuracy and robustness.

Authors:  Lenny Stoeldraijer; Coen van Duin; Leo van Wissen; Fanny Janssen
Journal:  Genus       Date:  2018-09-29
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  2 in total

1.  Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach.

Authors:  Xiaobai Zhu; Kenneth Q Zhou
Journal:  Eur Actuar J       Date:  2022-06-27

2.  Normalized lifespan inequality: disentangling the longevity-lifespan variability nexus.

Authors:  Iñaki Permanyer; Jiaxin Shi
Journal:  Genus       Date:  2022-01-10
  2 in total

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