Abstract
Human mortality modeling and forecasting are important study fields since mortality rates are essential in financial and social policy making. Among many others, Lee Carter (LC) model is one of the most popular stochastic method in mortality forecasting. Koissi and Shapiro fuzzified the standard LC model and eliminated the assumptions of homoscedasticity and the ambiguity on the size of the error term variances. In this study, a modified version of fuzzy LC model incorporating singular value decomposition (SVD) technique is proposed. Utilizing SVD instead of ordinary least squares in the fuzzy LC model allows the model to capture existing fluctuations in mortality rates and yields a better fit. The proposed method is applied to Finland mortality data for years 1925 to 2009. The results are compared with Koissi and Shapiro's fuzzy LC method and the standard LC method. Numerical findings show that proposed method gives statistically better results in generating small spreads and in estimating mortality rates when compared with Koissi and Shapiro's method.
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