International Statistical Review
- Internat. Statist. Rev.
- Volume 72, Number 2 (2004), 201-208.
Developing Official Stochastic Population Forecasts at the US Census Bureau
The U.S. Census Bureau is approaching a critical decision regarding a major facet of its methodology for forecasting the United States population. In the past, it has relied on alternative scenarios, low, medium, and high, to reflect varying interpretations of current trends in fertility, mortality, and international migration to forecast population. This approach has limitations that have been noted in the recent literature on population forecasting. Census Bureau researchers are embarking on an attempt to incorporate probabilistic reasoning to forecast prediction intervals around point forecasts to future dates. The current literature offers a choice of approaches to this problem. We are opting to employ formal time series modeling of parameters of fertility, mortality, and international migration, with stochastic renewal processes. The endeavor is complicated by the administrative necessity to produce a large amount of racial and Hispanic origin detail in the population, as well as the ubiquitous cross-categories of age and sex. As official population forecasts must meet user demand, we are faced with the added challenge of presenting and supporting the resulting product in a way that is comprehensible to users, many of whom have little or no comprehension of the technical forecasting literature, and are accustomed to simple, deterministic answers. We may well find a need to modify our strategy, depending on the realities that may emerge from the limitations of data, the administrative requirements of the product, and the diverse needs of our user community.
Internat. Statist. Rev., Volume 72, Number 2 (2004), 201-208.
First available in Project Euclid: 3 August 2004
Permanent link to this document
Zentralblatt MATH identifier
Long, John F.; Hollmann, Frederick W. Developing Official Stochastic Population Forecasts at the US Census Bureau. Internat. Statist. Rev. 72 (2004), no. 2, 201--208. https://projecteuclid.org/euclid.isr/1091543055