This article deals with the forecast accuracy and bias of population projections for 2,971 counties in the United States. It uses three different population projection techniques and data from 1950, 1960,1970, and 1980 to make two sets of 10-year projections and one set of 20-year projections. These projections are compared with census counts to determine forecast errors. The size, direction, and distribution of forecast errors are analyzed by size of place, rate of growth, and length of projection horizon.
The housing unit (HU) method is used by public and private agencies throughout the United States to make local population estimates. This article describes many of the different types of data and techniques that can be used in applying the HU method, and it discusses the strengths and weaknesses of each. Empirical evidence from four different states is provided, comparing the accuracy of HU population estimates with the accuracy of other commonly used estimation techniques. Several conclusions are drawn regarding the usefulness of the HU method for local population estimation.
The housing unit (HU) method is often characterized as inferior to other methods for estimating the population of states and local areas. We believe this characterization must be challenged. In this article we evaluate population estimates produced by the housing unit method and by three other commonly used methods: component 11, ratio correlation, and administrative records.
State populations in the United States are characterized by large differences in current growth rates and historical growth trends. What demographic factors account for these differences? Population growth has only three components: births, deaths, and migration. In this study, we estimated the contributions of births, deaths, and migration to changes in population size between 1950 and 1980 for the 48 contiguous states in the United States.
Many different techniques can be used for making population projections. Most fall into four general categories: trend extrapolation, ratio extrapolation, cohort-component and structural. Techniques within these categories differ considerably in terms of their complexity and sophistication. A common perception among producers (and users) of population projections is that complex and/or sophisticated techniques produce more accurate forecasts than simple and/or naive techniques.
Many studies have found that population forecast errors generally increase with the length of the forecast horizon, but none have examined this relationship in detail. Do errors grow linearly, exponentially, or in some other manner as the forecast horizon becomes longer? Does the error-horizon relationship differ by forecasting technique, launch year, size of place, or rate of growth? Do alternative measures of error make a difference? In this article we address these questions using two simple forecasting techniques and population data from 1900 to 1980 for states in the United States.
A number of studies in recent years have investigated elnpirical approaches to the production of confidence intervals for population projections. The critical assumption underlying these approaches is that the distribution of forecast errors remains stable over time. In this article, we evaluate this assumption by making population projections for states for a number of tilne periods during the 20th century, comparing these projections with census enumerations to deter~nine forecast errors, and analyzing the stability of the resulting error distributions over time.
This article develops three different models of migration for cohort-component projections, each using a different base (i.e., denominator) for migration rates. The differences in the resulting projections are analyzed, and a number of conclusions are drawn regarding the construction of migration rates for use in cohort-component population projections.
The housing unit method of population estimation is often characterized as being imprecise and having upward bias. In an earlier paper we argued that the method itself cannot be properly characterized by a particular level of precision or direction of bias. Only specific techniques of applying the method can have such characteristics. In that paper we presented several new techniques for estimating the number of households and average number of persons per household (PPH).
The housing unit method of population estimation is often characterized as being imprecise and having upward bias. We believe that the method itself cannot properly be categorized by a particular level of precision or direction of bias. Only specific techniques of applying the method can have such characteristics. In this paper we discuss several new techniques we have developed for estimating households and the average number of persons per household.