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Big data for twenty-first-century economic statistics / edited by Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Studies in income and wealth ; volume 79Publisher: Chicago ; London : The University of Chicago Press, [2022]Copyright date: © 2022Description: 1 Online-Ressource (xi, 489 Seiten) : DiagrammeISBN:
  • 9780226801391
Subject(s): Additional physical formats: 9780226801254 | Erscheint auch als: Big data for twenty-first-century economic statistics. Druck-Ausgabe Chicago : The University of Chicago Press, 2022. xi, 489 SeitenDDC classification:
  • 330.072/7
RVK: RVK: QH 240Local classification: Lokale Notation: wirt 3.0LOC classification:
  • HB143
  • HB143.5
DOI: DOI: 10.7208/chicago/9780226801391Online resources: Summary: Frontmatter -- Contents -- Prefatory Note -- Introduction: Big Data for Twenty- First- Century Economic Statistics: The Future Is Now -- I. Toward Comprehensive Use of Big Data in Economic Statistics -- 1. Reengineering Key National Economic Indicators -- 2. Big Data in the US Consumer Price Index -- 3. Improving Retail Trade Data Products Using Alternative Data Sources -- 4. From Transaction Data to Economic Statistics -- 5. Improving the Accuracy of Economic Measurement with Multiple Data Sources -- II. Uses of Big Data for Classification -- 6. Transforming Naturally Occurring Text Data into Economic Statistics -- 7. Automating Response Evaluation for Franchising Questions on the 2017 Economic Census -- 8. Using Public Data to Generate Industrial Classification Codes -- III. Uses of Big Data for Sectoral Measurement -- 9. Nowcasting the Local Economy -- 10. Unit Values for Import and Export Price Indexes -- 11. Quantifying Productivity Growth in the Delivery of Important Episodes of Care within the Medicare Program Using Insurance Claims and Administrative Data -- 12. Valuing Housing Services in the Era of Big Data -- IV. Methodological Challenges and Advances -- 13. Off to the Races -- 14. A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data -- 15. Estimating the Benefits of New Products -- Contributors -- Author Index -- Subject IndexSummary: 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 statisticsPPN: PPN: 1796134937Package identifier: Produktsigel: ZDB-23-DBV | ZDB-23-DGG | GBV-deGruyter-alles | ZDB-23-1
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