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Macroeconomic Forecasting in the Era of Big Data: Theory and Practice de Peter Fuleky

Macroeconomic Forecasting in the Era of Big Data: Theory and Practice: Advanced Studies in Theoretical and Applied Econometrics, cartea 52

Editat de Peter Fuleky
en Limba Engleză Paperback – 19 dec 2020
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.
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Specificații

ISBN-13: 9783030311520
ISBN-10: 303031152X
Ilustrații: XIII, 719 p. 80 illus., 62 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 1.01 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Advanced Studies in Theoretical and Applied Econometrics

Locul publicării:Cham, Switzerland

Cuprins

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.

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

Presents a comprehensive collection of big data tools used in macroeconomic forecasting. Surveys the most recent developments in the field. Offers algorithmic descriptions of big data techniques for forecasting. Useful as a reference, a textbook, and a resource for professional forecasters.