Big data for twenty-first-century economic statistics[electronic resource]
- 其他作者: Abraham, Katharine G.
- 出版: Chicago : The University of Chicago Press 2022.
- 稽核項: 1 online resource (xi, 489 p.) :ill., maps.
- 叢書名: NBER studies in income and wealth ;v. 79
- 標題: Economics , Economics Statistical methods -- Data processing. , Statistical methodsData processing. , Big data.
- ISBN: 022680139X , 9780226801391
- ISBN: 9780226801254
- 試查全文@TNUA:
- 附註: Includes bibliographical references and index.
- 摘要: "The measurement infrastructure for the production of economic statistics in the United States largely was established in the middle part of the 20th century. As has been noted by a number of commentators, the data landscape has changed in fundamental ways since this infrastructure was developed. Obtaining survey responses has become increasingly difficult, leading to increased data collection costs and raising concerns about the quality of the resulting data. At the same time, the economy has become more complex and users are demanding ever more timely and granular data. In this new environment, there is increasing interest in alternative sources of data that might allow the economic statistics agencies to better address users' demands for information. Recent years have seen a proliferation of natively digital data that have enormous potential for improving economic statistics. These include item-level transactional data on price and quantity from retail scanners or companies' internal systems, credit card records, bank account records, payroll records and insurance records compiled for private business purposes; data automatically recorded by sensors or mobile devices; and a growing variety of data that can be obtained from websites and social media platforms. Staggering volumes of digital information relevant to measuring and understanding the economy are generated each second by an increasing array of devices that monitor transactions and business processes as well as track the activities of workers and consumers. Incorporating these non-designed Big Data sources into the economic measurement infrastructure holds the promise of allowing the statistical agencies to produce more accurate, more timely and more disaggregated statistics, with lower burden for data providers and perhaps even at lower cost for the statistical agencies. The agencies already have begun to make use of novel data to augment traditional data sources. Modern data science methods for using
- 電子資源: https://dbs.tnua.edu.tw/login?url=https://www.degruyter.com/isbn/9780226801391
- 系統號: 005331770
- 資料類型: 電子書
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- 引用網址: 複製連結
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.
來源: Google Book
來源: Google Book
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