A cohort-based extension to the Lee-Carter model for mortality reduction factors

By A. Renshaw, Steven Haberman. 2006

783 citations recorded by the source corpus.

Subfields: Demography, General Health Professions, Health

View Paper: https://www.semanticscholar.org/paper/71ad7bfb9d5814649e5e23ce1b51863015c84640

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Citations in the corpus

A step-by-step guide to building two-population stochastic mortality models (2015, neutral): “Model M2: The Renshaw-Haberman Model (Renshaw and Haberman, 2006)”
A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States (2009, methodology): “Currie, Durban, and Eilers (2004) propose the use of B-splines and P-splines to fit the mortality surface: aylog m(t, x) B (x, t), i j i j i, j 9 Although we use the same model, Currie (2006) incorporates a penalty for lack of smoothness.”
Understanding, modelling and managing longevity risk: key issues and main challenges (2012, methodology): “Some relevant extensions of this approach have also been proposed, incorporating, for example, the cohort effect as in Renshaw and Haberman (2006) or Haberman and Renshaw (2006).”
Mortality Modelling and Forecasting: a Review of Methods (2008, methodology): “GLM approaches often employ the over-dispersed Poisson (Renshaw & Haberman, 2003b; Renshaw & Haberman, 2006), while Li et al. (forthcoming) use the negative binomial to take account of individual heterogeneity.”
The New Life Market (2013, neutral): “See also Swiss Re (2010a).”
Projecting the future burden of cancer: Bayesian age–period–cohort analysis with integrated nested Laplace approximations (2017, methodology): “4.2 The LC model We compare the projections and their calibration obtained by the Bayesian APC model to those obtained by a generalized version of the LC model (Lee and Carter, 1992; Renshaw and Haberman, 2006) with Poisson error structure where log(λi j ) = αi + βi · κ j .”
Smoothing and projecting age-specific probabilities of death by TOPALS (2012, positive): “Cohort effects can lead to non-linear developments (Renshaw and Haberman, 2006).”
Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States (2020, methodology): “(2004), Lee (2000), Renshaw and Haberman (2006)), principal component models were recently used to estimate and forecast mortality (e.g. Alexander et al.”
Machine learning techniques for mortality modeling (2017, methodology): “In Section 3 we analyze two standard models, the Lee-Carter [5] model and the Renshaw-Haberman [7] model, and we explain how machine learning techniques can be applied to investigate the adequacy of these models for a given dataset.”
Stochastic portfolio specific mortality and the quantification of mortality basis risk (2009, background): “JEL classification: G22; G23 Subject classification: IM10; IE43; IB10 Keywords: portfolio specific mortality, stochastic mortality models, mortality basis risk, longevity risk, Solvency 2”
A Two-Factor Model for Stochastic Mortality With Parameter Uncertainty: Theory and Calibration (2006, positive): “A recent paper by Renshaw and Haberman (2006) has adapted the Lee and Carter approach to incorporate a cohort effect and we anticipate more work along these lines in the coming years.”
Longevity risk management for life and variable annuities: The effectiveness of static hedging using longevity bonds and derivatives (2011, methodology): “Further developments were propsoed by Renshaw and Haberman (2003, 2006) [40, 41], who introduced a multifactor model and then extended it to incorporate a cohort effect.”
A comparative study of parametric mortality projection models (2011, methodology): “Also, in common with other studies involving these data, we have zero weighted the data cells for the cohort year of birth 1886: see Renshaw and Haberman (2006).”