Robust forecasting of mortality and fertility rates: A functional data approach

Authors:
Rob J. Hyndman;Md. Shahid Ullah
Affiliations:
Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia;Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia
Venue:
Computational Statistics & Data Analysis
Year:
2007

Citing 2
Cited 9

Monotonic smoothing splines fitted by cross validation

SIAM Journal on Scientific Computing
High breakdown estimators for principal components: the projection-pursuit approach revisited

Journal of Multivariate Analysis

Editorial: Statistics for Functional Data

Computational Statistics & Data Analysis
Modelling residuals dependence in dynamic life tables: A geostatistical approach

Computational Statistics & Data Analysis
Forecasting binary longitudinal data by a functional PC-ARIMA model

Computational Statistics & Data Analysis
Variational Bayesian functional PCA

Computational Statistics & Data Analysis
Intensive Computational Forecasting Approach to the Functional Demographic Lee Carter Model

Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
Nonparametric time series forecasting with dynamic updating

Mathematics and Computers in Simulation
Short-term sales forecasting with change-point evaluation and pattern matching algorithms

Expert Systems with Applications: An International Journal
Quantiles for finite and infinite dimensional data

Journal of Multivariate Analysis
Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

A new method is proposed for forecasting age-specific mortality and fertility rates observed over time. This approach allows for smooth functions of age, is robust for outlying years due to wars and epidemics, and provides a modelling framework that is easily adapted to allow for constraints and other information. Ideas from functional data analysis, nonparametric smoothing and robust statistics are combined to form a methodology that is widely applicable to any functional time series data observed discretely and possibly with error. The model is a generalization of the Lee-Carter (LC) model commonly used in mortality and fertility forecasting. The methodology is applied to French mortality data and Australian fertility data, and the forecasts obtained are shown to be superior to those from the LC method and several of its variants.