Demographic Forecasting / / Gary King, Federico Girosi.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Girosi, Federico,; King, Gary,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2018]; ©2008
• 出版年: 2018
• 語言: English
• 實物描述: 1 online resource
• Other title: Frontmatter --; Contents --; Figures --; Tables --; Preface --; Acknowledgments --; 1. Qualitative Overview --; Part I. Existing Methods for Forecasting Mortality --; 2. Methods without Covariates --; 3. Methods with Covariates --; Part II. Statistical Modeling --; 4. The Model --; 5. Priors over Grouped Continuous Variables --; 6. Model Selection --; 7. Adding Priors over Time and Space --; 8. Comparisons and Extensions --; Part III. Estimation --; 9. Markov Chain Monte Carlo Estimation --; 10. Fast Estimation without Markov Chains --; Part IV. Empirical Evidence --; 11. Illustrative Analyses --; 12. Comparative Analyses --; 13. Concluding Remarks --; Appendixes --; Bibliography --; Index
• 總結: 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
• 格式: Mode of access: Internet via World Wide Web.
• ISBN: 9780691186788; 9783110442502
• DOI: 10.1515/9780691186788?locatt=mode:legacy
• 訪問: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Gary King, Federico Girosi.

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...