The aim of the paper is to improve the Lee-Carter model performance developing a methodology able to refine its predictive accuracy. Considering relevant information the discrepancies between the real data and the Lee-Carter outputs, we model a measure of the fitting errors as a Cox-Ingersoll-Ross process. A new LC model is derived, called mLC. We apply the results over a fixed prediction span and with respect to the mortality data relating to the Italian females aged 18 and 65, chosen as examples of the model application. Through the backtesting procedure within a static framework, the model mLC proves itself to outperform the LC model.

Improving Lee-Carter forecasting: methodology and some results

Emilia Di Lorenzo;Marilena Sibillo
2018

Abstract

The aim of the paper is to improve the Lee-Carter model performance developing a methodology able to refine its predictive accuracy. Considering relevant information the discrepancies between the real data and the Lee-Carter outputs, we model a measure of the fitting errors as a Cox-Ingersoll-Ross process. A new LC model is derived, called mLC. We apply the results over a fixed prediction span and with respect to the mortality data relating to the Italian females aged 18 and 65, chosen as examples of the model application. Through the backtesting procedure within a static framework, the model mLC proves itself to outperform the LC model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/702927
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