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Related Experiment Videos

Forecasting mortality: a parameterized time series approach.

R McNown1, A Rogers

  • 1Population Program, University of Colorado, Boulder 80309.

Demography
|November 1, 1989
PubMed
Summary

This study forecasts U.S. mortality trends using model schedules and time series analysis. The method accurately predicts mortality patterns to 2000, aligning with Social Security Administration benchmarks.

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Area of Science:

  • Demography
  • Biostatistics
  • Actuarial Science

Background:

  • Historical U.S. mortality data from 1900-1985 presents complex patterns by age and sex.
  • Accurate mortality forecasting is crucial for public health planning and actuarial science.
  • Existing forecasting methods may not fully capture long-term mortality trend dynamics.

Purpose of the Study:

  • To develop a novel methodology for forecasting U.S. mortality.
  • To link parameterized model mortality schedules with time series analysis for improved predictions.
  • To evaluate the accuracy and sensibility of the developed mortality forecasts.

Main Methods:

  • Utilized parameterized model mortality schedules for concise historical data representation (1900-1985).
  • Applied modern time series methods to project mortality trends to the year 2000.
  • Incorporated flexibility to model trend changes and long-run mortality pattern shifts.

Main Results:

  • The proposed procedure generated medium-range mortality forecasts for the U.S.
  • Forecast accuracy was validated using standard forecast evaluation tests.
  • The forecasts demonstrated sensibility when compared with Social Security Administration projections.

Conclusions:

  • The integration of model mortality schedules and time series methods provides a robust approach to U.S. mortality forecasting.
  • This pilot study confirms the utility and accuracy of the developed forecasting procedure.
  • The methodology offers a flexible framework for anticipating future mortality trends and demographic shifts.

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