Title: Big Data for Twenty-First-Century Economic Statistics
Authors: Abraham K. G.
Jarmin R. S.
Moyer Brian C.
Shapiro M. D.
Keywords: Big Data
data processing
economics
machine learning
Statistical methods
большие данные
машинное обучение
обработка данных
статистические методы
экономика
Issue Date: 2022
Publisher: University of Chicago Press
Citation: Big Data for Twenty-First-Century Economic Statistics / edited by Brian C. Moyer [and other]. - Chicago : University of Chicago Press, 2022. - 1 file (8,49 Mb) (502 p.). - ISBN = 9780226801254, 9780226801391. - Текст : электронный
Abstract: 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 data users of economic statistics. It describes the deployment of Big Data to sol
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URI: http://repo.ssau.ru/jspui/handle/123456789/62459
ISBN: 9780226801254
9780226801391
ISSN: 
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Other Identifiers: 3104441
Appears in Collections:eBooks

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