Macroeconomic Forecasting in the Era of Big Data
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: | 9783030311490 |
|---|---|
| Sprache: | Englisch |
| Seitenzahl: | 719 |
| Produktart: | Gebunden |
| Herausgeber: | Fuleky, Peter |
| Verlag: | Springer International Publishing |
| Veröffentlicht: | 12.12.2019 |
| Untertitel: | Theory and Practice |
| Schlagworte: | Big Data Dimension reduction Dynamic factor models Estimation of common factors Macroeconomic forecasting Mixed frequency data sampling regressions Model forecast combination Penalized regression Shrinkage Vector autoregressions |
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.