Advanced Studies in Theoretical and Applied Econometrics Ser.: Macroeconomic Forecasting in the Era of Big Data : Theory and Practice (2019, Hardcover)
The lowest-priced brand-new, unused, unopened, undamaged item in its original packaging (where packaging is applicable).Packaging should be the same as what is found in a retail store, unless the item is handmade or was packaged by the manufacturer in non-retail packaging, such as an unprinted box or plastic bag.See details for additional description.
$205.68
Free Shipping
Get it by Monday, Mar 15 from USA, United States
• Brand New condition
• 30 day returns - Buyer pays return shipping
Number of Pages: 719. Weight: 2.64 lbs. Publication Date: 2019-12-12. Publisher: SPRINGER NATURE.
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.
Product Identifiers
Publisher
Springer
ISBN-10
303031149x
ISBN-13
9783030311490
eBay Product ID (ePID)
12039458399
Product Key Features
Format
Hardcover
Publication Year
2019
Language
English
Dimensions
Weight
44.5 Oz
Width
6.1in.
Length
9.3in.
Additional Product Features
Number of Volumes
1 Vol.
Table of Content
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.
Series Volume Number
52
Illustrated
Yes
Series
Advanced Studies in Theoretical and Applied Econometrics Ser.
Advanced Studies in Theoretical and Applied Econometrics Ser.: Macroeconomic Forecasting in the Era of Big Data : Theory and Practice (2019, Hardcover)