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- W3149795611 abstract "During the last fewdecades growth in life expectancy has resulted in increased pressure onpersonal and public finances. The increasing amount of attention paid onlongevity risk and funding for old age has created the need for precisemortality models and accurate mortality forecasts. Indeed, many attempts havebeen made to understand mortality and there has been a rich literature onmortality modeling which goes back to the very early years. The overall aim ofthis PhD thesis is to have a better understanding of mortality patterns andimprove the accuracy of future mortality projections. In this thesis, we applyvarious econometric and statistical techniques to mortality data from a widerange of developed countries including the Great Britain, the United States,Australia, Netherlands, Japan, France and Spain over the post-war period1950–2009. Contributions have been made to the existing literature with focus givento the forecasting perspective of models and to the analysis of cohort effects. In particular we applymethods familiar to the econometrics literature to the area of mortality wherethey are less applied. The four main chapters of the thesis link to each other in acomprehensive way. In Chapter 2, we apply a semiparametric local linearestimation framework to stochastic mortality models which frees the commonlyused Poisson assumption on number of deaths and improves upon the forecasting ability of the model. Then in Chapter 3 we introduce aflexible functional form approach aiming to capture cohort effects via the useof Legendre orthogonal polynomials in age and time dimensions. We allow forgreater flexibility in the model by considering two-dimensional polynomialsinstead of one-dimensional polynomials in either age or time dimension. In order to further understand the nature of cohort effects and beable to evaluate and compare the strength of cohort effects across differentcountries, in Chapter 4 of the thesis we develop a two-dimensional kernelsmoothing mortality model which enables us to analyze cohort effects in aquantitative manner. Finally, based on the empirical results from the firstthree main chapters, we conduct an investigation to compare the forecastingperformance among different types of mortality models. We conclude that toimprove forecast accuracy, more emphasis should be given to recent data overhistorical data during the forecasting process." @default.
- W3149795611 created "2021-04-13" @default.
- W3149795611 creator A5084846204 @default.
- W3149795611 date "2017-02-09" @default.
- W3149795611 modified "2023-09-23" @default.
- W3149795611 title "Techniques to Analyze and Forecast Mortality" @default.
- W3149795611 doi "https://doi.org/10.4225/03/589bbadfb9139" @default.
- W3149795611 hasPublicationYear "2017" @default.
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