Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this chapter we analyze theoretically the factors that determine the advantages from combining forecasts (for example, the degree of correlation between forecast errors and the relative size of the individual models' forecast error variances). Although the reasons for the success of simple combination schemes are poorly understood, we discuss several possibilities related to model misspecification, instability (non-stationarities) and estimation error in situations where the number of models is large relative to the available sample size. We discuss the role of combinations under asymmetric loss and consider combinations of point, interval and probability forecasts.
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ReDIF This chapter was published in: G. Elliott & C. Granger & A. Timmermann (ed.) , Elsevier, chapter 04, pages 135-196, 2006.
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This chapter was published in the following book, which is listed on IDEAS: G. Elliott & C. Granger & A. Timmermann (ed.), 2006.
"Handbook of Economic Forecasting,"
Handbook of Economic Forecasting,
Elsevier,
edition 1, volume 1, number 1, September.
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Franses, Ph.H.B.F. & Legerstee, R., 2007.
"A Manager's Perspective on Combining Expert and Model-based Forecasts,"
Research Paper
ERS-2007-083-MKT Revision, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
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