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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.
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.
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