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Forecasting Thai Mortality by Using the Lee-Carter Model

Forecasting Thai Mortality by Using the Lee-Carter Model APJRI 2016; 10(1): 91–105 Featured Article Natthasurang Yasungnoen* and Pairote Sattayatham Forecasting Thai Mortality by Using the Lee-Carter Model DOI 10.1515/apjri-2014-0042 Abstract: In this paper, we model the mortality rate in Thailand by using the Lee-Carter model. Three classical methods, i.e. Singular Value Decomposition (SVD), Weighted Least Square (WLS), and Maximum Likelihood Estimation (MLE) are used to estimate the parameters of the Lee-Carter model. With these methods, we investigate the goodness of fit for the mortality rate span- ning the period 2003 to 2012. The fitted models are compared. The autoregres- sive moving average (ARIMA) is used to forecast the general index and mortality rate the time period from 2013 to 2022. As a result, we also forecast Thai life expectancy at birth. Keywords: Mortality rate, Lee-Carter model, Life expectancy at birth, Singular Value Decomposition, Maximum Likelihood Estimation 1 Introduction At present, technological advances in medicine and public health have caused decline in the mortality rate. Forecasting the mortality rate is important in studying the trends of population; the death rate can be as important as the birth rate. In the insurance business, actuaries need to be able to create an accurate mortality table to determine insurance premiums and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asia-Pacific Journal of Risk and Insurance de Gruyter

Forecasting Thai Mortality by Using the Lee-Carter Model

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Publisher
de Gruyter
Copyright
Copyright © 2011–2019 by Walter de Gruyter GmbH
ISSN
2153-3792
DOI
10.1515/apjri-2014-0042
Publisher site
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Abstract

APJRI 2016; 10(1): 91–105 Featured Article Natthasurang Yasungnoen* and Pairote Sattayatham Forecasting Thai Mortality by Using the Lee-Carter Model DOI 10.1515/apjri-2014-0042 Abstract: In this paper, we model the mortality rate in Thailand by using the Lee-Carter model. Three classical methods, i.e. Singular Value Decomposition (SVD), Weighted Least Square (WLS), and Maximum Likelihood Estimation (MLE) are used to estimate the parameters of the Lee-Carter model. With these methods, we investigate the goodness of fit for the mortality rate span- ning the period 2003 to 2012. The fitted models are compared. The autoregres- sive moving average (ARIMA) is used to forecast the general index and mortality rate the time period from 2013 to 2022. As a result, we also forecast Thai life expectancy at birth. Keywords: Mortality rate, Lee-Carter model, Life expectancy at birth, Singular Value Decomposition, Maximum Likelihood Estimation 1 Introduction At present, technological advances in medicine and public health have caused decline in the mortality rate. Forecasting the mortality rate is important in studying the trends of population; the death rate can be as important as the birth rate. In the insurance business, actuaries need to be able to create an accurate mortality table to determine insurance premiums and

Journal

Asia-Pacific Journal of Risk and Insurance – de Gruyter

Published: Jan 1, 2016

Keywords: Mortality rate

References

  • A Poisson Log-Bilinear Regression Approach to the Construction of Projection Life Table
  • Simple Models for the Analysis is Association in Cross Classification Havingorders Categories
  • Modelling and Forecast the Time Series of US Mortality
  • Evaluating the Performance of the Lee Carter Mortality Forecast
  • Explaining Young Mortality
  • On Stochastic Mortality Modeling
  • An Empirical Study of Mortality Model in Taiwan