Macroeconomic Forecasting in the Era of Big Data Theory and Practice / [electronic resource] :
edited by Peter Fuleky.
- 1st ed. 2020.
- XIII, 719 p. 80 illus., 62 illus. in color. online resource.
- Advanced Studies in Theoretical and Applied Econometrics, 52 2214-7977 ; .
- Advanced Studies in Theoretical and Applied Econometrics, 52 .
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.
9783030311506
10.1007/978-3-030-31150-6 doi
Econometrics. Macroeconomics. Big data. Statistics . Quantitative research. Econometrics. Macroeconomics and Monetary Economics. Big Data. Statistics in Business, Management, Economics, Finance, Insurance. Data Analysis and Big Data.