Selecting and combining experts from survey forecasts

Julieta Fuentes, Pilar Poncela and Julio Rodríguez

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: Combining multiple forecasts provides gains in prediction accuracy. Therefore, with the aim of finding an optimal weighting scheme, several combination techniques have been proposed in the forecasting literature. In this paper we propose the use of sparse partial least squares (SPLS) as a method to combine selected individual forecasts from economic surveys. SPLS chooses the forecasters with more predictive power about the target variable, discarding the panelists with redundant information. We employ the Survey of Professional Forecasters dataset to explore the performance of different methods for combining forecasts: average forecasts, trimmed mean, regression based methods and regularized methods also in regression. The results show that selecting and combining forecasts yields to improvements in forecasting accuracy compared to the hard to beat average of forecasters.

Date: 2014-03
New Economics Papers: this item is included in nep-for
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