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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.

Part Ⅰ. Introduction 1. Sources and Types of Big Data for Macroeconomic Forecasting Part Ⅱ. Capturing Dynamic Relationships 2. Dynamic Factor Models 3. Factor Augmented Vector Autoregressions, Panel VARs, and Global VARs 4. Large Bayesian Vector Autoregressions 5. Volatility Forecasting in a Data Rich Environment 6. Neural Networks Part Ⅲ. Seeking Parsimony: Penalized Time Series Regression 7. Principal Component and Static Factor Analysis 8. Subspace Methods 9. Variable Selection and Feature Screening Part Ⅳ.Dealing with Model Uncertainty 11. Frequentist Averaging 12. Bayesian Model Averaging 13. Bootstrap Aggregating and Random Forest 14. Boosting 15. Density Forecasting 16. Forecast Evaluation Part Ⅴ. Further Issues 17. Unit Roots and Cointegration 18. Turning Points and Classification 19. Robust Methods for High-dimensional Regression and Covariance Matrix Estimation 20. Frequency Domain 21. Hierarchical Forecasting