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Big Data for Twenty First Century Economic Statistics 1st Edition by Katharine Abraham, Ron Jarmin, Brian Moyer, Matthew Shapiro ISBN 022680139X 9780226801391

Name: Big Data for Twenty First Century Economic Statistics 1st Edition by Katharine Abraham, Ron Jarmin, Brian Moyer, Matthew Shapiro ISBN 022680139X 9780226801391
SKU: EB-43049328
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Big Data for Twenty-First-Century Economic Statistics 1st Edition Katharine G. Abraham (Editor) Digital Instant Download

Author(s): Katharine G. Abraham (editor), Ron S. Jarmin (editor), Brian C. Moyer (editor), Matthew D. Shapiro (editor)

ISBN(s): 9780226801254, 022680125X

Edition: 1

File Details: PDF, 8.31 MB

Year: 2022

Language: English

SKU: EB-43049328 Category: Uncategorized Tags: Brian C. Moyer (editor), Katharine G. Abraham (editor), Matthew D. Shapiro (editor), Ron S. Jarmin (editor)

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Big Data for Twenty-First-Century Economic Statistics 1st Edition by Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, Matthew D. Shapiro – Ebook PDF Instant Download/Delivery: 022680139X, 9780226801391

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Product details:

ISBN 10: 022680139X

ISBN 13: 9780226801391

Author: Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, Matthew D. Shapiro

Big Data for Twenty-First-Century Economic Statistics 1st Edition: 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.

Big Data for Twenty-First-Century Economic Statistics 1st EditionTable of contents:

I. Toward Comprehensive Use of Big Data in Economic Statistics

Reengineering Key National Economic Indicators
Big Data in the US Consumer Price Index: Experiences and Plans
Improving Retail Trade Data Products Using Alternative Data Sources
From Transaction Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending
Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data

II. Uses of Big Data for Classification

Transforming Naturally Occurring Text Data into Economic Statistics: The Case of Online Job Vacancy Postings
Automating Response Evaluation for Franchising Questions on the 2017 Economic Census
Using Public Data to Generate Industrial Classification Codes

III. Uses of Big Data for Sectoral Measurement

Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity
Unit Values for Import and Export Price Indexes: A Proof of Concept
Quantifying Productivity Growth in the Delivery of Important Episodes of Care within the Medicare Program Using Insurance Claims and Administrative Data
Valuing Housing Services in the Era of Big Data: A User Cost Approach Leveraging Zillow Microdata

IV. Methodological Challenges and Advances

Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators
A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data
Estimating the Benefits of New Products

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