Big Data for Twenty-First-Century Economic Statistics
The papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement. The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with adm...
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| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Chicago
University of Chicago Press,
2022.
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| Series: | Studies in income and wealth.
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| Subjects: | |
| Online Access: | EBSCOhost Перейти в каталог НБ ТГУ |
| LEADER | 04498cam a2200481Mu 4500 | ||
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| 003 | OCoLC | ||
| 005 | 20250222070042.0 | ||
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| 020 | |a 022680139X | ||
| 020 | |a 9780226801391 |q (electronic bk.) | ||
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| 049 | |a MAIN | ||
| 100 | 1 | |a Abraham, Katharine G. |9 914147 | |
| 245 | 1 | 0 | |a Big Data for Twenty-First-Century Economic Statistics |h [electronic resource]. |
| 260 | |a Chicago |b University of Chicago Press, |c 2022. |9 706950 | ||
| 300 | |a 1 online resource (502 p.). | ||
| 490 | 1 | |a National Bureau of Economic Research Studies in Income and Wealth ; |v v.79 | |
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | 0 | |t Frontmatter -- |t Contents -- |t Prefatory Note -- |t Introduction: Big Data for Twenty- First- Century Economic Statistics: The Future Is Now -- |t I. Toward Comprehensive Use of Big Data in Economic Statistics -- |t 1. Reengineering Key National Economic Indicators -- |t 2. Big Data in the US Consumer Price Index -- |t 3. Improving Retail Trade Data Products Using Alternative Data Sources -- |t 4. From Transaction Data to Economic Statistics -- |t 5. Improving the Accuracy of Economic Measurement with Multiple Data Sources -- |t II. Uses of Big Data for Classification -- |t 6. Transforming Naturally Occurring Text Data into Economic Statistics -- |t 7. Automating Response Evaluation for Franchising Questions on the 2017 Economic Census -- |t 8. Using Public Data to Generate Industrial Classification Codes -- |t III. Uses of Big Data for Sectoral Measurement -- |t 9. Nowcasting the Local Economy -- |t 10. Unit Values for Import and Export Price Indexes -- |t 11. Quantifying Productivity Growth in the Delivery of Important Episodes of Care within the Medicare Program Using Insurance Claims and Administrative Data -- |t 12. Valuing Housing Services in the Era of Big Data -- |t IV. Methodological Challenges and Advances -- |t 13. Off to the Races -- |t 14. A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data -- |t 15. Estimating the Benefits of New Products -- |t Contributors -- |t Author Index -- |t Subject Index |
| 520 | |a The papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement. The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Data--such as scanner data on purchases, credit card transaction records, payroll information, and prices of various goods scraped from the websites of online sellers--has changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economic data to meet the needs of policymakers and other data users. This volume documents progress made toward that goal and the challenges to be overcome to realize the full potential of Big Data in the production of economic statistics. It describes the deployment of Big Data to solve both existing and novel challenges in economic measurement, and it will be of interest to statistical agency staff, academic researchers, and serious users of economic statistics. | ||
| 653 | 0 | |a Big data. | |
| 653 | 0 | |a Economics |x Statistical methods |x Data processing. | |
| 653 | 6 | |a Données volumineuses. | |
| 653 | 6 | |a Économie politique |x Méthodes statistiques |x Informatique. | |
| 653 | 7 | |a BUSINESS & ECONOMICS / General. |2 bisacsh | |
| 655 | 0 | |a EBSCO eBooks |9 905790 | |
| 655 | 4 | |a Electronic books. |9 899821 | |
| 700 | 1 | |a Jarmin, Ron S. |9 914148 | |
| 700 | 1 | |a Moyer, Brian C. |9 914149 | |
| 700 | 1 | |a Shapiro, Matthew D. |9 914150 | |
| 830 | 0 | |a Studies in income and wealth. |9 912831 | |
| 856 | 4 | 0 | |3 EBSCOhost |u https://www.lib.tsu.ru/limit/2023/EBSCO/3104441.pdf |
| 856 | |y Перейти в каталог НБ ТГУ |u https://koha.lib.tsu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=1014639 | ||
| 910 | |a EBSCO eBooks | ||
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