The Bureau of Economic and Business Research Population Program, under contract with the Florida Legislature, has been making three sets of population projections (low, medium, and high) for Florida and its counties for many years. Many decisions in both the public and private sectors are based on expectations of future population change. Planning for schools, hospitals, shopping centers, housing developments, electric power plants, and many other projects is strongly influenced by expected population growth or decline.
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 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. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two.
Population projections are judged primarily by their accuracy. The most commonly used measure for the precision component of accuracy is the mean absolute percent error (MAPE). Recently, the MAPE has been criticized for overstating forecast error and other error measures have been proposed. This study compares the MAPE with two alternative measures of forecast error, the Median APE and an M-estimator. In addition, the paper also investigates forecast bias.
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.
Population projections are widely used in both the public and private sectors for planning, budgeting, and analysis. For these purposes, projections are often needed for small areas such as census tracts, zip code areas or traffic analysis zones. Population size, growth constraints, shifting boundaries, and data availability create special problems for small-area projections, however, and very little is known about their forecasting performance.
The base period of a population forecast is the time period from which historical data are collected for the purpose of forecasting future population values. The length of the base period is one of the fundamental decisions made in preparing population forecasts, yet very few studies have investigated the effects of this decision on population forecast errors. In this article the relationship between the length of the base period and population forecast errors is analyzed, using three simple forecasting techniques and data from 1900 to 1980 for states in the United States.
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.
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.
Developments in economic theory over the last 20 years have placed decisions regarding female labor force participation (FLFP) and fertility within a model of household decision making (HDM). In this one period, static model utility is a function of child services (including both number and quality of children), market goods and services, and leisure. At the outset of their marriage a husband and wife adopt a utility-maximizing lifetime plan of fertility, market work, nonmarket activities, and consumption of goods and services, subject to income and time constraints.