| Demographic Forecasting Contributor(s): Girosi, Federico (Author), King, Gary (Author) |
|
 |
ISBN: 0691130957 ISBN-13: 9780691130958 Publisher: Princeton University Press OUR PRICE: $47.50 Product Type: Paperback - Other Formats Published: August 2008 Annotation: "A substantial contribution in terms of ideas and methods. This book introduces a new methodology for forecasting mortality that takes into account important predictors, formalizes the use of prior knowledge such as expert opinion, and produces estimates of forecasting uncertainty, using a Bayesian statistical framework. It also provides convincing evidence that the new methods can outperform some of the best alternatives available. The techniques have wide applicability."--German Rodriguez, Princeton University "This book is excellent and important."--Ronald Lee, University of California, Berkeley |
| Additional Information |
| BISAC Categories: - Social Science | Demography - Social Science | Statistics |
| Dewey: 304.640 |
| LCCN: 2008062102 |
| Physical Information: 0.65" H x 8.06" W x 9.66" (1.96 lbs) 288 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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.
|
