Overview
- Presents a comprehensive collection of big data tools used in macroeconomic forecasting.
- Surveys the most recent developments in the field.
- Offers algorithmic descriptions of big data techniques for forecasting.
- Useful as a reference, a textbook, and a resource for professional forecasters.
Part of the book series: Advanced Studies in Theoretical and Applied Econometrics (ASTA, volume 52)
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Table of contents (21 chapters)
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Front Matter
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Introduction
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Front Matter
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Capturing Dynamic Relationships
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Front Matter
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Seeking Parsimony
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Front Matter
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Dealing with Model Uncertainty
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Front Matter
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Keywords
- Big Data
- Macroeconomic forecasting
- Dimension reduction
- Shrinkage
- Model forecast combination
- Dynamic factor models
- Vector autoregressions
- Mixed frequency data sampling regressions
- Estimation of common factors
- Penalized regression
- Variable selection
- Feature screening
- Subspace methods
- Averaging
- Aggregation
- Unit roots
- Cointegration
- Forecasts
- Time varying parameters
About this book
This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.
Editors and Affiliations
About the editor
Bibliographic Information
Book Title: Macroeconomic Forecasting in the Era of Big Data
Book Subtitle: Theory and Practice
Editors: Peter Fuleky
Series Title: Advanced Studies in Theoretical and Applied Econometrics
DOI: https://doi.org/10.1007/978-3-030-31150-6
Publisher: Springer Cham
eBook Packages: Economics and Finance, Economics and Finance (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-31149-0Published: 12 December 2019
Softcover ISBN: 978-3-030-31152-0Published: 19 December 2020
eBook ISBN: 978-3-030-31150-6Published: 28 November 2019
Series ISSN: 1570-5811
Series E-ISSN: 2214-7977
Edition Number: 1
Number of Pages: XIII, 719
Number of Illustrations: 18 b/w illustrations, 62 illustrations in colour
Topics: Econometrics, Macroeconomics/Monetary Economics//Financial Economics, Big Data, Statistics for Business, Management, Economics, Finance, Insurance, Big Data/Analytics