Macroeconomic Forecasting in the Era of Big Data

Theory and Practice

Inbunden, Engelska, 2019

3 829 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

Produktinformation

Utgivningsdatum2019-12-12
Mått155 x 235 x 45 mm
Vikt1 256 g
FormatInbunden
SpråkEngelska
SerieAdvanced Studies in Theoretical and Applied Econometrics
Antal sidor719
Upplaga19001
FörlagSpringer Nature Switzerland AG
ISBN9783030311490

Tillhör följande kategorier

Matematisk statistik inom Naturvetenskap och teknik
Nationalekonomi inom Ekonomi och ledarskap
Makroekonomi inom Ekonomi och ledarskap
Mikroekonomi inom Ekonomi och ledarskap
Databaser inom Data och it

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.