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.
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.
Peter Fuleky is an Associate Professor of Economics with a joint appointment at the University of Hawaii Economic Research Organization (UHERO), and the Department of Economics at the University of Hawaii at Manoa. His research focuses on econometrics, time series analysis, and forecasting. He is a co-author of UHERO's quarterly forecast reports on Hawaii's economy. He obtained his Ph.D. degree in Economics at the University of Washington, USA.
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