TY - UNPB
T1 - Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principle Components
AU - Toczydlowska, Dorota
AU - Peters, Gareth
AU - Fung, Simon Man Chung
AU - Shevchenko, Pavel V.
PY - 2017/6/1
Y1 - 2017/6/1
N2 - In this study we develop a multi-factor extension of the family of Lee-Carter stochastic mortality models. 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. We undertake a detailed case study on the Human Mortality Database demographic data from European countries and we use the extracted features to better explain the term structure of mortality in the UK over time for male and female populations when compared to a pure Lee-Carter stochastic mortality model, demonstrating our feature extraction framework and consequent multi-factor mortality model improves both in sample fit and importantly out-off sample mortality forecasts by a non-trivial gain in performance.
AB - In this study we develop a multi-factor extension of the family of Lee-Carter stochastic mortality models. 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. We undertake a detailed case study on the Human Mortality Database demographic data from European countries and we use the extracted features to better explain the term structure of mortality in the UK over time for male and female populations when compared to a pure Lee-Carter stochastic mortality model, demonstrating our feature extraction framework and consequent multi-factor mortality model improves both in sample fit and importantly out-off sample mortality forecasts by a non-trivial gain in performance.
KW - Demographic
KW - Factor Model
KW - Feature Extraction
KW - Robust Estimation
KW - Stochastic Mortality Models
U2 - 10.2139/ssrn.2977306
DO - 10.2139/ssrn.2977306
M3 - Working paper
BT - Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principle Components
PB - SSRN
ER -