Forecasting Death Rates Using Exogenous Determinants

ABSTRACT Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality‐forecasting models be associated with real‐world trends in health‐related variables? Does inclusion of health‐related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle‐related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Rob J. Hyndman,et al.  Robust forecasting of mortality and fertility rates: A functional data approach , 2007, Comput. Stat. Data Anal..

[2]  D. Jones,et al.  Unemployment and mortality. , 1987, British medical journal.

[3]  Serena Ng,et al.  Evaluating latent and observed factors in macroeconomics and finance , 2006 .

[4]  H. Booth,et al.  Mortality Modelling and Forecasting: a Review of Methods , 2008, Annals of Actuarial Science.

[5]  M. Murphy.  Reexamining the Dominance of Birth Cohort Effects on Mortality. , 2010, Population and development review.

[6]  James W. Vaupel,et al.  The heritability of human longevity: A population-based study of 2872 Danish twin pairs born 1870–1900 , 1996, Human Genetics.

[7]  H. Frech,et al.  Is There a Link Between Pharmaceutical Consumption and Improved Health in OECD Countries? , 2012, PharmacoEconomics.

[8]  I. Leveson,et al.  The Production of Health, an Exploratory Study , 1969 .

[9]  James A. Thornton.  Estimating a health production function for the US: some new evidence , 2002 .

[10]  D. Barker.  Fetal and infant origins of adult disease , 2001, Monatsschrift Kinderheilkunde.

[11]  Kevin Dowd,et al.  A Two-Factor Model for Stochastic Mortality With Parameter Uncertainty: Theory and Calibration , 2006 .

[12]  Gary King,et al.  The future of death in America. , 2011, Demographic research.

[13]  F. Pampel.  Forecasting sex differences in mortality in high income nations : The contribution of smoking , 2005 .

[14]  Ronald Lee,et al.  Modeling and forecasting U. S. mortality , 1992 .

[15]  N. Keiding,et al.  Unemployment and mortality in Denmark, 1970-80. , 1987, British medical journal.

[16]  D. Leon,et al.  Trends in European Life Expectancy: a Salutary View Former Communist Countries of Eastern Europe , 2022 .

[17]  Modeling longevity risks using a principal component approach: A comparison with existing stochastic mortality models , 2010 .

[18]  Changes in the Age Distribution of Mortality Over the 20th Century , 2001 .

[19]  Steven Haberman,et al.  A cohort-based extension to the Lee-Carter model for mortality reduction factors , 2006 .

[20]  J Schwartz,et al.  Increased mortality in Philadelphia associated with daily air pollution concentrations. , 1992, The American review of respiratory disease.

[21]  Richard Plat,et al.  On Stochastic Mortality Modeling , 2009 .

[22]  O. Linton,et al.  EFFICIENT SEMIPARAMETRIC ESTIMATION OF THE FAMA-FRENCH MODEL AND EXTENSIONS , 2012 .

[23]  James H. Stock,et al.  Dynamic Factor Models , 2011 .

[24]  J. Bai,et al.  Determining the Number of Factors in Approximate Factor Models , 2000 .

[25]  J. Westerlund.  Testing for Error Correction in Panel Data , 2006 .