During the last few
decades growth in life expectancy has resulted in increased pressure on
personal and public finances. The increasing amount of attention paid on
longevity risk and funding for old age has created the need for precise
mortality models and accurate mortality forecasts. Indeed, many attempts have
been made to understand mortality and there has been a rich literature on
mortality modeling which goes back to the very early years. The overall aim of
this PhD thesis is to have a better understanding of mortality patterns and
improve the accuracy of future mortality projections. In this thesis, we apply
various econometric and statistical techniques to mortality data from a wide
range of developed countries including the Great Britain, the United States,
Australia, Netherlands, Japan, France and Spain over the post-war period
1950–2009. Contributions have been made to the existing literature with focus given
to the forecasting perspective of models and to the analysis of cohort effects. In particular we apply
methods familiar to the econometrics literature to the area of mortality where
they are less applied. <br>
The four main chapters of the thesis link to each other in a
comprehensive way. In Chapter 2, we apply a semiparametric local linear
estimation framework to stochastic mortality models which frees the commonly
used Poisson assumption on number of deaths and improves upon the forecasting ability of the model. Then in Chapter 3 we introduce a
flexible functional form approach aiming to capture cohort effects via the use
of Legendre orthogonal polynomials in age and time dimensions. We allow for
greater flexibility in the model by considering two-dimensional polynomials
instead of one-dimensional polynomials in either age or time dimension. In order to further understand the nature of cohort effects and be
able to evaluate and compare the strength of cohort effects across different
countries, in Chapter 4 of the thesis we develop a two-dimensional kernel
smoothing mortality model which enables us to analyze cohort effects in a
quantitative manner. Finally, based on the empirical results from the first
three main chapters, we conduct an investigation to compare the forecasting
performance among different types of mortality models. We conclude that to
improve forecast accuracy, more emphasis should be given to recent data over
historical data during the forecasting process. <br>
<br
