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• W2249933342 abstract "While macroeconomic survey forecasts are widely available at the level of individual experts, it is not clear how to optimally combine a set of forecasts to a “consensus” prediction. This is mainly due to the characteristics of the data, such as the large-dimensional predictor space, many missing values, and potential individual and aggregate level biases of the survey forecasts. We argue that regression trees are very well adapted to these features and propose to use them as a novel forecast combination device. Our empirical analysis of data from the Philadelphia Fed’s Survey of Professional Forecasters demonstrates that in combination with bagging, tree-based forecast combination outperforms equally weighted combination for the majority of time series and forecast horizons." @default.
• W2249933342 created "2016-06-24" @default.
• W2249933342 creator A5007381307 @default.
• W2249933342 creator A5070045352 @default.
• W2249933342 date "2011-01-01" @default.
• W2249933342 modified "2023-09-26" @default.
• W2249933342 title "Learning from Micro-Level Expert Forecasts: Real-Time Data, Regression Trees, and Bagging" @default.
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• W2249933342 doi "https://doi.org/10.2139/ssrn.1916760" @default.
• W2249933342 hasPublicationYear "2011" @default.
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