A Bayesian forecasting model: predicting U.S. male mortality.
This article presents a Bayesian approach to forecast mortality rates. This approach formalizes the Lee-Carter method as a statistical model accounting for all sources of variability. Markov chain Monte Carlo methods are used to fit the model and to sample from the posterior predictive distribution. This paper also shows how multiple imputations can be readily incorporated into the model to handle missing data and presents some possible extensions to the model. The methodology is applied to U.S. male mortality data. Mortality rate forecasts are formed for the period 1990-1999 based on data from 1959-1989. These forecasts are compared to the actual observed values. Results from the forecasts show the Bayesian prediction intervals to be appropriately wider than those obtained from the Lee-Carter method, correctly incorporating all known sources of variability. An extension to the model is also presented and the resulting forecast variability appears better suited to the observed data.
Division of Biostatistics, University of Texas School of Public Health at Houston, Houston, TX 77030, USA. claudia.pedroza@uth.tmc.edu
Claudia
C
Pedroza
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D005544:Forecasting / Q000379:methods; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009026:Mortality / Q000639:trends; D014481:United States / Q000453:epidemiology