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Stochastic methods in population forecasting.

J M Alho

    International Journal of Forecasting
    |December 1, 1990
    PubMed
    Summary
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    This study introduces a stochastic demographic forecasting model. It uses random vital rates to predict future populations, offering new insights into fertility and mortality forecasting accuracy.

    Area of Science:

    • Demography
    • Population Studies
    • Statistical Modeling

    Background:

    • Traditional demographic forecasting relies on deterministic models.
    • Accurate population projections are crucial for policy and resource allocation.
    • Understanding uncertainty in vital rates is key for reliable forecasts.

    Purpose of the Study:

    • To develop a stochastic version of the demographic cohort-component method.
    • To model future population sizes as non-linear functions of random vital rates.
    • To provide a framework for calculating prediction intervals for population forecasts.

    Main Methods:

    • Developed a stochastic cohort-component model.
    • Utilized linear approximations and simulation to approximate joint distributions of vital rates.
    Keywords:
    Age DistributionAge FactorsAmericasBiasComparative StudiesDemographic FactorsDeveloped CountriesError SourcesEstimation TechnicsFertilityMeasurementMethodological StudiesModels, TheoreticalMortalityNorth AmericaNorthern AmericaPopulationPopulation CharacteristicsPopulation DynamicsPopulation ForecastResearch MethodologySex DistributionSex FactorsStudiesUnited States

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  • Introduced a volatility measure for fertility.
  • Compared expert opinion with extrapolation for mortality forecasting.

    • Main Results:

    • The stochastic model allows for the calculation of approximate prediction intervals for births.
    • Analysis highlights the need for empirical research on vital rate autocorrelations and cross-correlations.
    • Forecasting declining mortality and fluctuating fertility present distinct challenges.
    • Expert opinion in US mortality forecasts showed potential for systematic bias compared to extrapolation.

    Conclusions:

    • Stochastic modeling provides a more realistic approach to population forecasting by incorporating uncertainty in vital rates.
    • Further empirical research is needed to improve the accuracy of stochastic demographic models.
    • The study offers a new volatility measure for fertility and contrasts forecasting challenges.
    • Past expert mortality forecasts may be biased, suggesting a need for revised forecasting methodologies.