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, principal components, and Bayesian regressions to a large panel of monthly U.S. macroeconomic data to forecast key variables …
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The aim of this paper is to construct a forecasting model oriented on predicting basic macroeconomic variables, namely: the GDP growth rate, the unemployment rate, and the consumer price inflation. In order to select the set of the best regressors, Bayesian Averaging of Classical Estimators...
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Uncertainty is inherent to forecasting and assessing the uncertainty surrounding a point forecast is as important as the forecast itself. Following Cornec (2010), a method to assess the uncertainty around the indicator models used at OECD to forecast GDP growth of the six largest member...
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In this paper, the authors develop a new tool to improve the short-term forecasting of real GDP growth in the euro area and Japan. This new tool, which uses unrestricted mixed-data sampling (U-MIDAS) regressions, allows an evaluation of the usefulness of a wide range of indicators in predicting...
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