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At least 19 recordsLinked to original sources
PubMed1983

Improving the accuracy of migration age detail in multiple-area population forecasts.

Population projections are often required for many geographical areas, and must be prepared with maximal computer and minimal analytical effort. At the same time, realistic age detail forecasts require a flexible means of treating age-specific net migration. This report presents a migration projection technique compatible with these constraints. A simplified version of Pittenger's model is used, where future migration patterns are automatically assigned from characteristics of historical patterns. A comparative test of age pattern accuracy for 1970-1980 indicates that this technique is superior to the commonly used plus-minus adjustment to historical rates.

Adolescent↗
PubMed1991

An empirical analysis of the effect of length of forecast horizon on population forecast errors.

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. We find that in most instances there is a linear or nearly linear relationship between forecast accuracy and the length of the forecast horizon, but no consistent relationship between bias and the length of the horizon. We believe that these results provide useful information regarding the nature of population forecast errors.

Humans↗
PubMed1990

The relationship between the length of the base period and population forecast errors.

"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. It is found that increasing the length of the base period up to 10 years improves forecast accuracy, but that further increases generally have little additional effect. The only exception to this finding is long-range forecasts of rapidly growing states, in which a longer base period substantially improves forecast accuracy for two of the forecasting techniques."

Americas↗
PubMed1986

Methods for national population forecasts: a review.

"Three widely used classes of methods for forecasting national populations are reviewed: demographic accounting/cohort-component methods for long-range projections, statistical time series methods for short-range forecasts, and structural modeling methods for the simulation and forecasting of the effects of policy changes. In each case, the major characteristics, strengths, and weaknesses of the methods are described. Factors that place intrinsic limits on the accuracy of population forecasts are articulated. Promising lines of additional research by statisticians and demographers are identified for each class of methods and for population forecasting generally."

Cohort Studies↗
PubMed1986

Population forecasts and confidence intervals for Sweden: a comparison of model-based and empirical approaches.

This paper compares several methods of generating confidence intervals for forecasts of population size. Two rest on a demographic model for age-structured populations with stochastic fluctuations in vital rates. Two rest on empirical analyses of past forecasts of population sizes of Sweden at five-year intervals from 1780 to 1980 inclusive. Confidence intervals produced by the different methods vary substantially. The relative sizes differ in the various historical periods. The narrowest intervals offer a lower bound on uncertainty about the future. Procedures for estimating a range of confidence intervals are tentatively recommended. A major lesson is that finitely many observations of the past and incomplete theoretical understanding of the present and future can justify at best a range of confidence intervals for population projections. Uncertainty attaches not only to the point forecasts of future population, but also to the estimates of those forecasts' uncertainty.

Forecasting↗
PubMed1996

On the utility of population forecasts.

Many customers demand population forecasts, particularly for small areas. Although the forecast evaluation literature is extensive, it is dominated by a focus on accuracy. We go beyond accuracy by examining the concept of forecast utility in an evaluation of a sample of 2,709 counties and census tracts. We find that forecasters provide "value-added" knowledge for areas experiencing rapid change or areas with relatively large populations. For other areas, reduced value is more common than added value. Our results suggest that new forecasting strategies and methods such as composite modeling may substantially improve forecast utility.

Bias↗
PubMed1978

Population forecasts for health planning: an assessment of the state of the art.

Health planning agencies make use of age-sex detailed forecasts of population. This article evaluates sources of forecasts, notes the essential role of assumptions in forecasts, and indicates the general quality of techniques for projecting components of population change. It clarifies for health planners what demographers can--and can not--deliver.

Demography↗
PubMed1994

Stochastic population forecasts for the United States: beyond high, medium, and low.

"This article presents and implements a new method for making stochastic population forecasts that provide consistent probability intervals. We blend mathematical demography and statistical time series methods to estimate stochastic models of fertility and mortality based on U.S. data back to 1900 and then use the theory of random-matrix products to forecast various demographic measures and their associated probability intervals to the year 2065. Our expected total population sizes agree quite closely with the Census medium projections, and our 95 percent probability intervals are close to the Census high and low scenarios. But Census intervals in 2065 for ages 65+ are nearly three times as broad as ours, and for 85+ are nearly twice as broad. In contrast, our intervals for the total dependency and youth dependency ratios are more than twice as broad as theirs, and our ratio for the elderly dependency ratio is 12 times as great as theirs. These items have major implications for policy, and these contrasting indications of uncertainty clearly show the limitations of the conventional scenario-based methods."

Age Factors↗
PubMed1992

Stochastic population forecasts and their uses.

"The properties and uses of stochastic forecasts are discussed here. For linear stochastic projections, we show how the computation of forecast moments and the statistical distribution of forecasts depend on the multiplicative and autoregressive structure of the dynamics. Both scalar and vector projection methods are discussed, and their similarities are explored. Next we discuss the uses of stochastic forecasts, arguing that it is important to relate forecasts to the specific decision-making criteria of particular forecast users. The example of [the U.S. system of] Social Security is used to show how a dynamic programming approach may be used to explore alternative decisions in a probabilistic context."

Americas↗
PubMed1988

Stability over time in the distribution of population forecast errors.

A number of studies in recent years have investigated empirical 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 time periods during the 20th century, comparing these projections with census enumerations to determine forecast errors, and analyzing the stability of the resulting error distributions over time. These data are then used to construct and test empirical confidence limits. We find that in this sample the distribution of absolute percentage errors remained relatively stable over time and data on past forecast errors provided very useful predictions of future forecast errors.

Demography↗
PubMed1997

A theory of technophysio evolution, with some implications for forecasting population, health care costs, and pension costs.

We argue that over the past 300 years human physiology has been undergoing profound environmentally induced changes made possible by numerous advances in technology. These changes, which we call technophysio evolution, increased body size by over 50%, and greatly improved the robustness and capacity of vital organ systems. Because technophysio evolution is still ongoing, it is relevant to forecasts of longevity and morbidity and, therefore, to forecasts of the size of the elderly population and pension and health care costs.

Europe↗
PubMed1992

Forecasting U.S. population totals with the Box-Jenkins approach.

"The use of the Box-Jenkins approach for forecasting the population of the United States up to the year 2080 is discussed. It is shown that the Box-Jenkins approach is equivalent to a simple trend model when making long-range predictions for the United States. An investigation of forecasting accuracy indicates that the Box-Jenkins method produces population forecasts that are at least as reliable as those done with more traditional demographic methods."

Americas↗
PubMed1996

Bayesian forecasting in paediatric populations.

Bayesian forecasting offers several important advantages for dosage individualisation in children, although, unlike for adults, its use in this population is much lower. Indeed, currently Bayesian methods are underused in this patient population. The paucity of paediatric population pharmacokinetic parameters, and the unavailability of specific clinical pharmacokinetic software for the whole paediatric population, are the main limitations to the application of Bayesian methods in these patients. When these problems have been overcome, this approach will allow clinicians to achieve therapeutic concentrations more readily, faster and more precisely, thus making the methodology highly attractive in the paediatric setting.

Adolescent↗
PubMed1992

Stochastic demographic forecasting.

"This paper describes a particular approach to stochastic population forecasting, which is implemented for the U.S.A. through 2065. Statistical time series methods are combined with demographic models to produce plausible long run forecasts of vital rates, with probability distributions. The resulting mortality forecasts imply gains in future life expectancy that are roughly twice as large as those forecast by the Office of the Social Security Actuary.... Resulting stochastic forecasts of the elderly population, elderly dependency ratios, and payroll tax rates for health, education and pensions are presented."

Adult↗