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Macroeconomic forecasting in the era...

Fuleky, Peter.

Macroeconomic forecasting in the era of big datatheory and practice /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Macroeconomic forecasting in the era of big dataedited by Peter Fuleky.
Reminder of title:	theory and practice /
other author:	Fuleky, Peter.
Published:	Cham :Springer International Publishing :2020.
Description:	xiii, 719 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Economic forecasting.
Online resource:	https://doi.org/10.1007/978-3-030-31150-6
ISBN:	9783030311506$q(electronic bk.)

Macroeconomic forecasting in the era of big datatheory and practice /
Macroeconomic forecasting in the era of big datatheory and practice /[electronic resource] :edited by Peter Fuleky. - Cham :Springer International Publishing :2020. - xiii, 719 p. :ill., digital ;24 cm. - Advanced studies in theoretical and applied econometrics,v.521570-5811 ;. - Advanced studies in theoretical and applied econometrics ;v.48..

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.

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.

ISBN: 9783030311506$q(electronic bk.)

Standard No.: 10.1007/978-3-030-31150-6doiSubjects--Topical Terms:

182801
Economic forecasting.

LC Class. No.: HB3730 / .M337 2020

Dewey Class. No.: 330.0112

Macroeconomic forecasting in the era of big datatheory and practice /
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505 0 $a 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.
520 $a 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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