About this book
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.
Big Data Macroeconomic forecasting Dimension reduction Shrinkage Model forecast combination Dynamic factor models Vector autoregressions Mixed frequency data sampling regressions Estimation of common factors Penalized regression Variable selection Feature screening Subspace methods Averaging Aggregation Unit roots Cointegration Forecasts Time varying parameters
Springer Nature Switzerland AG 2020
Economics and Finance
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