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. To assess the accuracy of the predictions of future vital rates, we 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 306-314 |
| Number of pages | 9 |
| Journal | Journal of the American Statistical Association |
| Volume | 80 |
| Issue number | 390 |
| DOIs | |
| State | Published - Jun 1985 |
Funding
* Juha M. Alho is Statistician, Institute of Occupational Health, Haartman-inkatu 1, SF-00290 Helsinki 29, Finland. Bruce D. Spencer is Assistant Pro- fessor, School of Education, Northwestern University, Evanston, IL 60201, This article is based on part of Alho’s doctoral dissertation, under the supervision of Spencer (Program in Probability and Statistics, Northwestern University). The authors are grateful to Northwestern University, the American Scandinavian Foundation, and the Finnish Cultural Foundation for financial support during various phases of this study. Discussions with Jerry Sacks were instrumental in the formulation of the approximately linear models (Section 3).
Keywords
- Census bureau
- Leslie model
- Mixed estimation
- Population forecasting
- Population projections
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty