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A1368
Title: Robust probabilistic PCA and dynamic factor models in mortality Authors:  Dorota Toczydlowska - UCL (United Kingdom)
Pavel Shevchenko - Maquarie University (Australia)
Gareth Peters - University College London (United Kingdom) [presenting]
Abstract: A multi factor extension of the family of Lee-Carter stochastic mortality models is developed. We build upon the time, period and cohort stochastic model structure to extend it to include exogenous observable demographic features that can be used as additional factors to improve model fit and forecasting accuracy. We develop a dimension reduction feature extraction framework which a) employs projection based techniques of dimensionality reduction; in doing this we also develop b) a robust feature extraction framework that is amenable to different structures of demographic data; c) we analyse demographic data sets from the patterns of missingness and the impact of such missingness on the feature extraction, and d) introduce a class of multi-factor stochastic mortality models incorporating time, period, cohort and demographic features, which we develop within a Bayesian statespace estimation framework; finally e) we develop an efficient combined Markov chain and filtering framework for sampling the posterior and forecasting.