Macroeconomic Forecasting in the Era of Big Data

Theory and Practice
1. Auflage 2020
ISBN: 978-3-030-31149-0
Verlag: Springer International Publishing

Buch, Englisch, Band 52, 719 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 1256 g

Reihe: Advanced Studies in Theoretical and Applied Econometrics

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.

Zielgruppe

Research

Autoren/Hrsg.

Fuleky, Peter

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsprognose
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Makroökonomie
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie

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.

Über Autor(innen)

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.