Stochastic Modeling and Forecasting of Health Changes in the U.S. Population
34 Pages Posted: 21 Sep 2015
Date Written: June 23, 2014
Abstract
This paper proposes a model for self-assessed health at an aggregate level that allows to generate age- and gender-specific stochastic forecasts of future health. We decompose health status into a time effect and an age effect. We then further decompose the time effect into observed macroeconomic quantities (GDP and unemployment rate) and an unobserved latent time factor. We use data on the U.S. population's self-assessed health for both males and females to estimate the model. The estimation results show that trends in health can be largely captured by trends in the observed macroeconomic quantities. Next, based on forecasts of the observed and the unobserved time effects, using a vector auto regression (VAR) model, we present forecasts for future health together with the corresponding forecasting uncertainty, showing that there is no clear future trend upward or downward. A backtesting analysis suggests that our approach with macroeconomic quantities significantly improves the forecasting accuracy for future health development compared with a simple extrapolation based approach. It also outperforms the model without taking into account observed variables.
Keywords: Stochastic process, Lee-Carter model, Macroeconomic fluctuations, Forecasts
JEL Classification: I15, C32, E32
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