Uncertain Population Forecasting

TLDR

Population forecast errors stem from inaccuracies in the initial population and in future vital‑rate predictions. The study investigates how these errors propagate through the Leslie growth model and develops prediction intervals for future population sizes. Using a parametric statistical model, the authors derive vital‑rate predictions, assess model misspecification and parameter‑estimate errors, incorporate expert opinion via mixed estimation, and apply robust regression to evaluate misspecification effects, then propagate these errors through the Leslie model to generate prediction intervals that are compared with Census Bureau high‑low bounds. The resulting prediction intervals for U.S.

Abstract

"Errors in population forecasts arise from errors in the jump-off population and errors in the predictions of future vital rates. The propagation of these errors through the linear (Leslie) growth model is studied, and prediction intervals for future population are developed. For U.S. national forecasts, the prediction intervals are compared with the U.S. Census Bureau's high-low intervals. " In order to assess the accuracy of the predictions of vital rates, the authors "derive the predictions from a parametric statistical model and estimate the extent of model misspecification and errors in parameter estimates. Subjective, expert opinion, so important in real forecasting, is incorporated with the technique of mixed estimation. A robust regression model is used to assess the effects of model misspecification."