 When to choose the simple average in forecast combinationSebastian M. Blanc and Thomas Setzer Journal of Business Research, 2016, vol. 69, issue 10, 3951-3962 Abstract: Numerous forecast combination techniques have been proposed. However, these do not systematically outperform a simple average (SA) of forecasts in empirical studies. Although it is known that this is due to instability of learned weights, managers still have little guidance on how to solve this “forecast combination puzzle”, i.e., which combination method to choose in specific settings. We introduce a model determining the yet unknown asymptotic out-of-sample error variance of the two basic combination techniques: SA, where no weightings are learned, and so-called optimal weights that minimize the in-sample error variance. Using the model, we derive multi-criteria boundaries (considering training sample size and changes of the parameters which are estimated for optimal weights) to decide when to choose SA. We present an empirical evaluation which illustrates how the decision rules can be applied in practice. We find that using the decision rules is superior to all other considered combination strategies. Keywords: Forecast combination; Simple average; Optimal weights; Structural changes (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:69:y:2016:i:10:p:3951-3962 DOI: 10.1016/j.jbusres.2016.05.013 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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