A Quantitative comparison of the Lee-Carter model under different types of non-Gaussian Innovations

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      <subfield code="a">A Quantitative comparison of the Lee-Carter model under different types of non-Gaussian Innovations</subfield>
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      <subfield code="a">In the classical Lee-Carter model, the mortality indices that are assumed to be a random walk model with drift are normally distributed. However, for the long-term mortality data, the error terms of the Lee-Carter model and the mortality indices have tails thicker than those of a normal distribution and appear to be skewed. This study therefore adopts five non-Gaussian distributions Students t-distribution and its skew extension (i.e., generalised hyperbolic skew Students t-distribution), one finite-activity Lévy model (jump diffusion distribution), and two infinite-activity or pure jump models (variance gamma and normal inverse Gaussian) to model the error terms of the Lee-Carter model. With mortality data from six countries over the period 1900-2007, both in-sample model selection criteria (e.g., Bayesian information criterion, Kolmogorov Smirnov test, Anderson Darling test, Cramérvon-Mises test) and out-of-sample projection errors indicate a preference for modelling the Lee-Carter model with non-Gaussian innovations</subfield>
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      <subfield code="g">03/10/2011 Tomo 36 Número 4  - 2011 , p. 675-696</subfield>
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