Optimal Combination of Survey Forecasts
AbstractWe consider the problem of optimally combining individual forecasts of gross domestic product (GDP) and inflation from the Survey of Professional Forecasters (SPF) dataset for the Euro Area. Contrary to the common practice of using equal combination weights, we compute optimal weights which minimize the mean square forecast error (MSFE) in the case of point forecasts and maximize a logarithmic score in the case of density forecasts. We show that this is a viable strategy even when the number of forecasts to combine gets large, provided we constrain these weights to be positive and to sum to one. Indeed, this enforces a form of shrinkage on the weights which ensures good out-of-sample performance of the combined forecasts.
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Bibliographic InfoPaper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 9096.
Date of creation: Aug 2012
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- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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- Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2014. "Selecting and combining experts from survey forecasts," Statistics and Econometrics Working Papers ws140905, Universidad Carlos III, Departamento de Estadística y Econometría.
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