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.
Inhaltsverzeichnis
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.
