In this paper, we propose a procedure for reducing the uncertainty about mortality projections, on the basis of a log bilinear Poisson Lee Carter model (Renshaw and Haberman 2003). In the literature, to provide confidence intervals for forecasted quantities, simulation techniques have been used, because of the non-linear nature of the quantities under consideration (i.e. Brouhns, N., Denuit, M., van Keilegom, I. 2005, Renshaw, A.E., Haberman, S. 2008). In that respect, we take into account the bootstrap simulation approach to measure the uncertainty affecting the mortality projections. In particular, we intend to make efficient the bootstrap procedure by using a specific variance reducing technique, the so-called stratified sampling. The results will be shown in the numerical applications.
Efficient Bootstrap applied to the Poisson Log-Bilinear Lee Carter Model
RUSSOLILLO, Maria;D'AMATO, VALERIA;
2009
Abstract
In this paper, we propose a procedure for reducing the uncertainty about mortality projections, on the basis of a log bilinear Poisson Lee Carter model (Renshaw and Haberman 2003). In the literature, to provide confidence intervals for forecasted quantities, simulation techniques have been used, because of the non-linear nature of the quantities under consideration (i.e. Brouhns, N., Denuit, M., van Keilegom, I. 2005, Renshaw, A.E., Haberman, S. 2008). In that respect, we take into account the bootstrap simulation approach to measure the uncertainty affecting the mortality projections. In particular, we intend to make efficient the bootstrap procedure by using a specific variance reducing technique, the so-called stratified sampling. The results will be shown in the numerical applications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
