Where do you live? For many people this seemingly simple question doesn’t have a simple answer. Some retirees spend winters in Florida or Arizona and summers in New York or Minnesota. Others buy an RV and move from place to place, with no fixed place of residence. College students spend part of the year in their college towns and part in their home towns. Migrant farm workers often move from place to place over the course of a year, spending no more than a few weeks or months at any given location.

Population forecasts for subcounty areas are used for a wide variety of planning and budgeting purposes. Given the importance of many of these uses, it is essential to investigate which techniques and procedures produce the most accurate forecasts. In this report, we describe several simple trend extrapolation techniques and several averages and composite methods based on those techniques. We evaluate the precision and bias of forecasts derived from these techniques using data from 1970–2005 for subcounty areas in Florida.

The effects of population size and growth rate on population forecast accuracy have been well documented. For example, we know that small places generally have larger errors than large places; that errors are generally higher for places with high growth rates than places with low growth rates; and that size of error generally remains more stable over time than does the direction of error. In this paper, we delve more deeply into these relationships using data for 2,482 counties in the United States and expand the analysis to include a third explanatory variable, prior forecast error.

The housing unit method is the most commonly used method for making small-area population estimates in the United States and is widely used in other countries as well. These estimates are used for a variety of budgeting, planning, and analytical purposes in both the public and private sectors; consequently, detailed evaluations of their accuracy are essential. In this study, we evaluate the precision and bias of April 1, 2000 population estimates for counties and subcounty areas in Florida.

Using the design of a natural experiment, this study investigated the effects of the political spectacle of 2002 Florida Gubernatorial candidate Janet Reno’s “Red Truck Tour,” which lasted from February - March 2002. Voting intentions and support for women’s issues were examined using the theoretical perspectives of political spectacle, direct effects, and gender effects. This study predicted a positive impact of candidate support and a shift in undecided

A number of studies have dealt with the use of time series models to develop confidence
intervals for population forecasts. Most have focused solely on national-level models and only a
few have considered the accuracy of the resulting forecasts. In this study, we take this research
in a new direction by constructing time series models for several states in the United States and
evaluating the resulting population forecasts. Using annual population estimates from 1900 to
2000, we develop a variety of forecasts and investigate the impact of differences in model

Paper presented at the annual meeting of the Southern Demographic Association, Hilton Head, South Carolina

This paper evaluates summary measures of population projection accuracy and bias for a large
sample of counties and county equivalents in the continental United States over the period 1900–
2000. The analysis has two primary purposes. The first is to investigate the relationship between
accuracy and bias and the length of the projection horizon and base period. The second is to
compare different trend extrapolation techniques with respect to their forecasting performance,

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals based on models that incorporate the stochastic nature of the forecasting process or on empirical analyses of past forecast errors. In this paper, we develop and test empirical prediction intervals for county population forecasts in the United States.

In this study, we describe an approach that can be used to estimate the demographic impact of hurricanes and other natural disasters, provide a detailed assessment of the 2004 hurricane season in Florida, compare the 2004 hurricanes with Hurricanes Andrew (1992) and Katrina (2005), and draw several conclusions regarding the likely impact of hurricanes and other natural disasters on future population growth.