Forecasting U.S. Mortality: A Comparison of Box-Jenkins ARIMA and Structural Time Series Models

AbstractThis article compares two methodologies for modeling and forecasting statistical time series models of demographic processes: Box-Jenkins ARIMA and structural time series analysis. The Lee-Carter method is used to construct nonlinear demographic models of U.S. mortality rates for the total population, gender, and race and gender combined. Single time varying parameters of k, the index of mortality, are derived from these model and fitted and forecasted using the two methodologies. Forecasts of life expectancy at birth, e0, are generated from these indexes of k. Results show marginal differences in fit and forecasts between the two statistical approaches with a slight advantage to structural models. Stability across models for both methodologies offers support for the robustness of this approach to demographic forecasting.