TY  - BOOK
AU  - Fuleky,Peter
ED  - SpringerLink (Online service)
TI  - Macroeconomic Forecasting in the Era of Big Data: Theory and Practice 
T2  - Advanced Studies in Theoretical and Applied Econometrics,
SN  - 9783030311506
AV  - HB139-141 
U1  - 330.015195 23
PY  - 2020///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Econometrics
KW  - Macroeconomics
KW  - Big data
KW  - Statistics
KW  - Quantitative research
KW  - Macroeconomics and Monetary Economics
KW  - Big Data
KW  - Statistics in Business, Management, Economics, Finance, Insurance
KW  - Data Analysis and Big Data
N1  - 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
N2  - 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
UR  - https://eresourcesptsl.ukm.remotexs.co/user/login?url=https://doi.org/10.1007/978-3-030-31150-6
ER  -