A Survey of Census Bureau Population Projection Methods

Long, John F.; McMillen, David Byron

doi:10.1007/978-94-009-4011-6_7

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…For subnational areas, migration is often the major determinant of population growth (Smith and Ahmed 1990). It is also the most difficult component of growth to forecast accurately because state and local migration rates are subject to much greater volatility than are either fertility or mortality rates (e.g., Kulkami and Pol 1994;Long and McMillen 1987;Nakosteen 1989). Since migration rates vary markedly by age, forecast errors are likely to be larger for some age groups than for others.…”

Section: Expectationsmentioning

confidence: 99%

An Evaluation of Population Projections by Age

Smith¹,

Tayman²

2003

Demography

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A number of studies have evaluated the accuracy ofprojections of the size of the total population, but few have considered the accuracy of projections by age group. For many purposes, however, the relevant variable is the population of a particular age group, rather than the population as a whole. We investigated the precision and bias of a variety of age-group projections at the national and state levels in the United States andfor counties in Florida. We also compared the accuracy of state and county projections that were derivedfrom full-blown applications of the cohort-component method with the accuracy of projections that were derived from a simpler, less data-intensive version of the method. We found that age-group error patterns are different for national projections than for subnational projections; that errors are substantially larger for some age groups than for others; that differences in errors among age groups decline as the projection horizon becomes longer, and that differences in methodological complexity have no consistent impact on the precision and bias of age-group projections.Population projections are used for a wide variety of planning and budgeting purposes.

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Section: Expectationsmentioning

confidence: 99%

An Evaluation of Population Projections by Age

Smith¹,

Tayman²

2003

Demography

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Section: Distributional Mosaic Modelsmentioning

confidence: 99%

“…The most extensive literature and most elaborate models of this type are in the area of multi-regional human population projection models (Rogers 1975(Rogers , 1985Rees and Wilson 1977;Woods and Rees 1986;Long and McMillen 1987). Fully developed population models may incorporate thousands of endogenous and exogenous variables in tens of geographic units (e.g., Long and McMillen 1987). Comparable, but much less complex spatial projection models have been applied in modelling other animal and plant populations (e.g., Usher and Williamson 1970;Cuff and Hardman 1980;Hobbs and Hobbs 1987) at small spatial scales.…”

Section: Distributional Mosaic Modelsmentioning

confidence: 99%

A review of models of landscape change

1989

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Models of landscape change may serve a variety of purposes, from exploring the interaction of natural processes to evaluating proposed management treatments. These models can be categorized as either whole landscape models, distributional landscape models, or spatial landscape models, depending on the amount of detail included in the models. Distributional models, while widely used, exclude spatial detail important for most landscape ecological research. Spatial models require substantial data, now more readily available, via remote sensing, and more easily manipulated, in geographical information systems. In spite of these technical advances, spatial modelling is poorly developed, largely because landscape change itself is poorly understood.To facilitate further development of landscape models I suggest (1) empirical multivariate studies of landscape change, (2) modelling of individual landscape processes, (3) explicit study of the effect of model scale on model behavior, and (4) 'scaling-up' results of studies, on smaller land areas, that have landscape relevance.

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“…The National Income and Product Accounts contain this and other identities. Another example: Systems of national and regional demographic accounts -counts of stocks and flows of people among demographic and/or social statuses such as age and occupational categories (Long and McMillen, 1987) -lead to the population transformation identity:…”

Section: Formal Forecasting Modelsmentioning

confidence: 99%

Forecasting

Land¹

2015

International Encyclopedia of the Social &Amp; Behavioral Sciences

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Forecasting comprises of the formation of expectations about future states or processes of social entities, such as populations or subpopulations thereof, and associated entities, such as economies and political and social institutions. This article commences by describing and giving examples of empirical applications of four methods of forecasting that have been developed, and are widely used, in the social sciences: leading indicators, quantitative trend analysis, formal forecasting models, and qualitative (theoretical-institutional-scenario) analysis and forecasts. This is followed by a section on limitations of forecasting. All social science forecasts are subject to deterioration of their accuracy with increasing temporal distance from the initiation point of the forecasts. This is due to the complexity of social systems to which forecasting models and the data from which they are estimated are only an approximation, the evolving nature of such systems, and the fact that the forecasts sometimes are just elements in the social system to which some actors respond, thus changing their behavior. It is concluded that forecasts should incorporate prediction intervals that grow with increases in the forecast horizon and the forecasts should be updated frequently.

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A Survey of Census Bureau Population Projection Methods

Cited by 7 publications

References 39 publications

An Evaluation of Population Projections by Age

An Evaluation of Population Projections by Age

A review of models of landscape change

Forecasting

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