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Forecast Combinations

Listed author(s):
  • Marco Aiolfi

    ()

    (Goldman Sachs Asset Management)

  • Carlos Capistrán

    ()

    (Banco de México)

  • Allan Timmermann

    ()

    (University of California, San Diego and CREATES)

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.

(This abstract was borrowed from another version of this item.)

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Paper provided by Department of Economics and Business Economics, Aarhus University in its series CREATES Research Papers with number 2010-21.

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Length: 33
Date of creation: 06 May 2010
Handle: RePEc:aah:create:2010-21
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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