Macroeconomic Forecasting in the Era of Big Data

Theory and Practice

Buch | Hardcover

XIII, 719 Seiten

2019 | 1st ed. 2020
Springer International Publishing (Verlag)
978-3-030-31149-0 (ISBN)

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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.

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.

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.

Erscheinungsdatum	19.12.2019
Reihe/Serie	Advanced Studies in Theoretical and Applied Econometrics
Zusatzinfo	XIII, 719 p. 80 illus., 62 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	1262 g
Themenwelt	Wirtschaft ► Allgemeines / Lexika
Themenwelt	Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie
Schlagworte	Aggregation • Averaging • Big Data • Cointegration • dimension reduction • dynamic factor models • Estimation of common factors • Feature screening • Forecasts • Macroeconomic Forecasting • Mixed frequency data sampling regressions • Model forecast combination • Penalized regression • Shrinkage • Subspace Methods • Time varying parameters • Unit Roots • Variable selection • Vector autoregressions
ISBN-10	3-030-31149-X / 303031149X
ISBN-13	978-3-030-31149-0 / 9783030311490
Zustand	Neuware