This paper presents three demographic models useful for projections of social sector demand. The first model is a probabilistic national population forecast. The second model forecasts the population by age and educational level using data from a single census while the third model examines the effects of changes in population age structure on social sector demand. Publishing Agency: United Nations (UN).

Population change and population forecasts are receiving considerable attention from governmental planners and policy-makers, as well as from the private sector. Old patterns of population redistribution, industrial location, labor-force participation, household formation, and fertility are changing. The resulting uncertainty has increased interest in forecasting because mere extrapolations of past trends are proving inadequate. In the United States of America popUlation forecasts received even more attention after federal agencies began distributing funds for capital infrastructure to state and local governments on the basis of projected future populations. If the national government had based those funding decisions on locally prepared projections, the optimism of local officials would have resulted in billions of dollars worth of excess capacity in sewage treatment plants alone. Cabinet-level inquiries concluded that the U. S. Department of Commerce should (1) assume the responsibility for developing a single set of projections for use whenever future population was a consideration in federal spending decisions and (2) develop methods which incorporate both economic and demographic factors causing population change. Neither the projections prepared by economists at the Bureau of Economic Analysis nor those prepared by demographers at the Bureau of the Census were considered satisfactory because neither method adequately recognized the intertwined nature of demographic and economic change. Against this background, the American Statistical Association (ASA) and the U. S.

Originally published in 1989. ECESIS consists of 51 regional econometric models (one for each state and the District of Columbia) and a multiregional demographic model. Its distinguishing feature is the linking of sophisticated demographic accounts with sophisticated structural econometric models. This book, looking at how strong the interactions are between population dynamics and economic activity, determines to what extent the simultaneous economic-demographic interregional model provides improved projection and simulation properties over regional economic and demographic models used independently of one another.

This open access book presents new developments in the field of demographic forecasting, covering both mortality, fertility and migration. For each component emerging methods to forecast them are presented. Moreover, instruments for forecasting evaluation are provided. Bayesian models, nonparametric models, cohort approaches, elicitation of expert opinion, evaluation of probabilistic forecasts are some of the topics covered in the book. In addition, the book is accompanied by complementary material on the web allowing readers to practice with some of the ideas exposed in the book. Readers are encouraged to use this material to apply the new methods to their own data. The book is an important read for demographers, applied statisticians, as well as other social scientists interested or active in the field of population forecasting. Professional population forecasters in statistical agencies will find useful new ideas in various chapters.

Population ageing has been going on for many decades, but population shrinking is a rather new phenomenon. The population of Germany, as in many other countries, has passed a plateau and is currently shrinking. Demographic change is a challenge for infrastructure planning due to the longevity of infrastructure capital and the need to match supply and demand in order to ensure cost-efficiency. This book summarises the findings of the INFRADEM project team, a multidisciplinary research group that worked together to estimate the effects of demographic change on infrastructure demand. Economists, engineers and geographers present studies from top-down and bottom-up perspectives, focusing on Germany and two selected regions: Hamburg and Mecklenburg-Western Pomerania. The contributors employed a broad range of methods, including an overlapping-generations model for Germany, regional input-output models, an energy systems model, and a spatial model of the transportation infrastructure.

This comprehensive work examines the latest developments in the growing field of household demography focusing on household analysis and modeling. Chapters examine the full range of stages in household projection-including data collection, data analysis, and selection of a projection model. Complete with numerous illustrations, this book is practical rather than conceptual with its exploration of existing models and concrete applications. Topics include historical trends, theories of household formation and dissolution, event-history analysis, multistate models, housing market models, labor market models and much more.

Demographic Forecasting introduces new statistical tools that can greatly improve forecasts of population death rates. Mortality forecasting is used in a wide variety of academic fields, and for policymaking in global health, social security and retirement planning, and other areas. Federico Girosi and Gary King provide an innovative framework for forecasting age-sex-country-cause-specific variables that makes it possible to incorporate more information than standard approaches. These new methods more generally make it possible to include different explanatory variables in a time-series regression for each cross section while still borrowing strength from one regression to improve the estimation of all. The authors show that many existing Bayesian models with explanatory variables use prior densities that incorrectly formalize prior knowledge, and they show how to avoid these problems. They also explain how to incorporate a great deal of demographic knowledge into models with many fewer adjustable parameters than classic Bayesian approaches, and develop models with Bayesian priors in the presence of partial prior ignorance. By showing how to include more information in statistical models, Demographic Forecasting carries broad statistical implications for social scientists, statisticians, demographers, public-health experts, policymakers, and industry analysts. Introduces methods to improve forecasts of mortality rates and similar variables Provides innovative tools for more effective statistical modeling Makes available free open-source software and replication data Includes full-color graphics, a complete glossary of symbols, a self-contained math refresher, and more