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Using the Lee–Carter Method to Forecast Mortality for Populations with Limited Data*
International Statistical Review, 2004 Summary The Lee–Carter method for modeling and forecasting mortality has been shown to work quite well given long time series of data. Here we consider how it can be used when there are few observations at uneven intervals. Assuming that the underlying model is correct and that the mortality index follows a random walk with drift, we find the method ...
Li, Nan +2 more
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DemografieThis article discusses the use of the Lee-Carter model, one of the most widely used methods for mortality forecasting. It also discusses its modified versions, namely the cohort and coherent variants, which were developed to improve the forecast accuracy
David Morávek
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Comparative Study of Lee Carter and Arch Model in Modelling Female Mortality in Nigeria
UMYU Scientifica Journal, 2022 Using Nigeria mortality data from 2009 to 2020, this study compares and contrasts how well the Lee-Carter and ARCH models performed. Singular value decomposition (SVD) method, Langrage multiplier test, and autoregressive conditional heteroskedasticity ...
Aliyu Umar Shelleng +3 more
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Generalization of stochastic mortality models to improve mortality prediction in life insurance and pension funds [PDF]
تصمیم گیری و تحقیق در عملیات, 2023 Purpose: Mortality is a dynamic process that completes over time and is a fundamental issue in life insurance, pension fund, health insurance, and in general any issue related to financial planning that deals with the longevity of individuals. Therefore,
Shirin Shoaee +1 more
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Longevity risk—Its financial impact on pensions
Scientific African, 2022 We study the financial impact of longevity risk on defined benefit (DB) pension plan liabilities using the 2010 Ghana population census, and the Lee-Carter model. We compare the usual Lee-Carter model to an extended version. While we observe that Ghana’s
Nana Poku Appiagyei Nantwi +2 more
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Bayesian Population Forecasting: Extending the Lee-Carter Method [PDF]
Demography, 2015 AbstractIn this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model.
Wisniowski, Arkadiusz (Arek) +4 more
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Thirty years on: A review of the Lee-Carter method for forecasting mortality
International Journal of Forecasting, 2022 The introduction of the Lee-Carter (LC) method marked a breakthrough in mortality forecasting, providing a simple yet powerful data-driven stochastic approach. The method has the merit of capturing the dynamics of mortality change by a single time index that is invariably linear.
Ugofilippo Basellini +2 more
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Evaluation of Lee–Carter model to breast cancer mortality prediction in China and Pakistan
Frontiers in Oncology, 2023 BackgroundPrecise breast cancer–related mortality forecasts are required for public health program and healthcare service planning. A number of stochastic model–based approaches for predicting mortality have been developed.
Sumaira Mubarik +4 more
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Evaluating the performance of the lee-carter method for forecasting mortality [PDF]
Demography, 2001 Abstract Lee and Carter (LC) published a new statistical method for forecasting mortality in 1992. This paper examines its actual and hypothetical forecast errors, and compares them with Social Security forecast errors. Hypothetical historical projections suggest that LC tended to underproject gains, but by less than did Social ...
R, Lee, T, Miller
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Lee-Carter method for forecasting mortality for Peruvian Population
Selecciones Matemáticas, 2021 In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality rates.
J. Cerda-Hernández, A. Sikov
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