Demographic Forecasting / / 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...
Saved in:
Superior document: | Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013 |
---|---|
VerfasserIn: | |
Place / Publishing House: | Princeton, NJ : : Princeton University Press, , [2018] ©2008 |
Year of Publication: | 2018 |
Language: | English |
Online Access: | |
Physical Description: | 1 online resource |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
9780691186788 |
---|---|
ctrlnum |
(DE-B1597)501912 (OCoLC)1076458038 |
collection |
bib_alma |
record_format |
marc |
spelling |
Girosi, Federico, author. aut http://id.loc.gov/vocabulary/relators/aut Demographic Forecasting / Gary King, Federico Girosi. Princeton, NJ : Princeton University Press, [2018] ©2008 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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 restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star 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 Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021) Demography. Mortality Forecasting Methodology. Mortality Statistical methods. SOCIAL SCIENCE / Demography. bisacsh King, Gary, author. aut http://id.loc.gov/vocabulary/relators/aut Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013 9783110442502 https://doi.org/10.1515/9780691186788?locatt=mode:legacy https://www.degruyter.com/isbn/9780691186788 Cover https://www.degruyter.com/cover/covers/9780691186788.jpg |
language |
English |
format |
eBook |
author |
Girosi, Federico, Girosi, Federico, King, Gary, |
spellingShingle |
Girosi, Federico, Girosi, Federico, King, Gary, Demographic Forecasting / 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 |
author_facet |
Girosi, Federico, Girosi, Federico, King, Gary, King, Gary, King, Gary, |
author_variant |
f g fg f g fg g k gk |
author_role |
VerfasserIn VerfasserIn VerfasserIn |
author2 |
King, Gary, King, Gary, |
author2_variant |
g k gk |
author2_role |
VerfasserIn VerfasserIn |
author_sort |
Girosi, Federico, |
title |
Demographic Forecasting / |
title_full |
Demographic Forecasting / Gary King, Federico Girosi. |
title_fullStr |
Demographic Forecasting / Gary King, Federico Girosi. |
title_full_unstemmed |
Demographic Forecasting / Gary King, Federico Girosi. |
title_auth |
Demographic Forecasting / |
title_alt |
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 |
title_new |
Demographic Forecasting / |
title_sort |
demographic forecasting / |
publisher |
Princeton University Press, |
publishDate |
2018 |
physical |
1 online resource |
contents |
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 |
isbn |
9780691186788 9783110442502 |
url |
https://doi.org/10.1515/9780691186788?locatt=mode:legacy https://www.degruyter.com/isbn/9780691186788 https://www.degruyter.com/cover/covers/9780691186788.jpg |
illustrated |
Not Illustrated |
dewey-hundreds |
300 - Social sciences |
dewey-tens |
300 - Social sciences, sociology & anthropology |
dewey-ones |
304 - Factors affecting social behavior |
dewey-full |
304.6/40112 |
dewey-sort |
3304.6 540112 |
dewey-raw |
304.6/40112 |
dewey-search |
304.6/40112 |
doi_str_mv |
10.1515/9780691186788?locatt=mode:legacy |
oclc_num |
1076458038 |
work_keys_str_mv |
AT girosifederico demographicforecasting AT kinggary demographicforecasting |
status_str |
n |
ids_txt_mv |
(DE-B1597)501912 (OCoLC)1076458038 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013 |
is_hierarchy_title |
Demographic Forecasting / |
container_title |
Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013 |
author2_original_writing_str_mv |
noLinkedField noLinkedField |
_version_ |
1806143273878683648 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04909nam a22006975i 4500</leader><controlfield tag="001">9780691186788</controlfield><controlfield tag="003">DE-B1597</controlfield><controlfield tag="005">20210830012106.0</controlfield><controlfield tag="006">m|||||o||d||||||||</controlfield><controlfield tag="007">cr || ||||||||</controlfield><controlfield tag="008">210830t20182008nju fo d z eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780691186788</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1515/9780691186788</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-B1597)501912</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1076458038</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-B1597</subfield><subfield code="b">eng</subfield><subfield code="c">DE-B1597</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">nju</subfield><subfield code="c">US-NJ</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">SOC006000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">304.6/40112</subfield><subfield code="2">22</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Girosi, Federico, </subfield><subfield code="e">author.</subfield><subfield code="4">aut</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Demographic Forecasting /</subfield><subfield code="c">Gary King, Federico Girosi.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Princeton, NJ : </subfield><subfield code="b">Princeton University Press, </subfield><subfield code="c">[2018]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2008</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">text file</subfield><subfield code="b">PDF</subfield><subfield code="2">rda</subfield></datafield><datafield tag="505" ind1="0" ind2="0"><subfield code="t">Frontmatter -- </subfield><subfield code="t">Contents -- </subfield><subfield code="t">Figures -- </subfield><subfield code="t">Tables -- </subfield><subfield code="t">Preface -- </subfield><subfield code="t">Acknowledgments -- </subfield><subfield code="t">1. Qualitative Overview -- </subfield><subfield code="t">Part I. Existing Methods for Forecasting Mortality -- </subfield><subfield code="t">2. Methods without Covariates -- </subfield><subfield code="t">3. Methods with Covariates -- </subfield><subfield code="t">Part II. Statistical Modeling -- </subfield><subfield code="t">4. The Model -- </subfield><subfield code="t">5. Priors over Grouped Continuous Variables -- </subfield><subfield code="t">6. Model Selection -- </subfield><subfield code="t">7. Adding Priors over Time and Space -- </subfield><subfield code="t">8. Comparisons and Extensions -- </subfield><subfield code="t">Part III. Estimation -- </subfield><subfield code="t">9. Markov Chain Monte Carlo Estimation -- </subfield><subfield code="t">10. Fast Estimation without Markov Chains -- </subfield><subfield code="t">Part IV. Empirical Evidence -- </subfield><subfield code="t">11. Illustrative Analyses -- </subfield><subfield code="t">12. Comparative Analyses -- </subfield><subfield code="t">13. Concluding Remarks -- </subfield><subfield code="t">Appendixes -- </subfield><subfield code="t">Bibliography -- </subfield><subfield code="t">Index</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">restricted access</subfield><subfield code="u">http://purl.org/coar/access_right/c_16ec</subfield><subfield code="f">online access with authorization</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="538" ind1=" " ind2=" "><subfield code="a">Mode of access: Internet via World Wide Web.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">In English.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Demography.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Mortality</subfield><subfield code="x">Forecasting</subfield><subfield code="x">Methodology.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Mortality</subfield><subfield code="x">Statistical methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SOCIAL SCIENCE / Demography.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">King, Gary, </subfield><subfield code="e">author.</subfield><subfield code="4">aut</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Title is part of eBook package:</subfield><subfield code="d">De Gruyter</subfield><subfield code="t">Princeton University Press eBook-Package Backlist 2000-2013</subfield><subfield code="z">9783110442502</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1515/9780691186788?locatt=mode:legacy</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.degruyter.com/isbn/9780691186788</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="3">Cover</subfield><subfield code="u">https://www.degruyter.com/cover/covers/9780691186788.jpg</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">978-3-11-044250-2 Princeton University Press eBook-Package Backlist 2000-2013</subfield><subfield code="c">2000</subfield><subfield code="d">2013</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_BACKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_CL_SN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EBACKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EBKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_ECL_SN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EEBKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_ESSHALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_PPALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_SSHALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_STMALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-deGruyter-alles</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA11SSHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA12STME</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA13ENGE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA17SSHEE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA5EBK</subfield></datafield></record></collection> |