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Macroeconomic Forecasting in the Era of Big Data : Theory and Practice / edited by Peter Fuleky.
- Format:
- Book
- Series:
- Advanced studies in theoretical and applied econometrics 1570-5811 ; 52.
- Advanced Studies in Theoretical and Applied Econometrics, 1570-5811 ; 52
- Language:
- English
- Subjects (All):
- Econometrics.
- Macroeconomics.
- Big data.
- Statistics.
- Macroeconomics/Monetary Economics//Financial Economics.
- Big Data.
- Statistics for Business, Management, Economics, Finance, Insurance.
- Big Data/Analytics.
- Local Subjects:
- Econometrics.
- Macroeconomics/Monetary Economics//Financial Economics.
- Big Data.
- Statistics for Business, Management, Economics, Finance, Insurance.
- Big Data/Analytics.
- Physical Description:
- 1 online resource.
- Edition:
- First edition 2020.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2020.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- Introduction: Sources and Types of Big Data for Macroeconomic Forecasting
- Capturing Dynamic Relationships: Dynamic Factor Models
- Factor Augmented Vector Autoregressions, Panel VARs, and Global VARs
- Large Bayesian Vector Autoregressions
- Volatility Forecasting in a Data Rich Environment
- Neural Networks
- Seeking Parsimony: Penalized Time Series Regression
- Principal Component and Static Factor Analysis
- Subspace Methods
- Variable Selection and Feature Screening
- Dealing with Model Uncertainty: Frequentist Averaging
- Bayesian Model Averaging
- Bootstrap Aggregating and Random Forest
- Boosting
- Density Forecasting
- Forecast Evaluation
- Further Issues: Unit Roots and Cointegration
- Turning Points and Classification
- Robust Methods for High-dimensional Regression and Covariance Matrix Estimation
- Frequency Domain
- Hierarchical Forecasting.
- Other Format:
- Printed edition:
- ISBN:
- 9783030311506
- Access Restriction:
- Restricted for use by site license.
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