Macroeconomic Forecasting in the Era of Big Data
Peter Fuleky (Hrsg.)
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Springer International Publishing
Sozialwissenschaften, Recht, Wirtschaft / Volkswirtschaft
Beschreibung
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.
Kundenbewertungen
Big Data, Unit roots, Forecasts, Dimension reduction, Time varying parameters, Penalized regression, Model forecast combination, Cointegration, Averaging, Estimation of common factors, Dynamic factor models, Vector autoregressions, Macroeconomic forecasting, Subspace methods, Variable selection, Aggregation, Shrinkage, Feature screening, Mixed frequency data sampling regressions