When and when not to use optimal model averaging

Michael Schomaker () and Christian Heumann ()
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Michael Schomaker: University of Cape Town
Christian Heumann: Ludwig-Maximilians Universität München

Statistical Papers, 2020, vol. 61, issue 5, No 20, 2240 pages

Abstract: Abstract Traditionally model averaging has been viewed as an alternative to model selection with the ultimate goal to incorporate the uncertainty associated with the model selection process in standard errors and confidence intervals by using a weighted combination of candidate models. In recent years, a new class of model averaging estimators has emerged in the literature, suggesting to combine models such that the squared risk, or other risk functions, are minimized. We argue that, contrary to popular belief, these estimators do not necessarily address the challenges induced by model selection uncertainty, but should be regarded as attractive complements for the machine learning and forecasting literature, as well as tools to identify causal parameters. We illustrate our point by means of several targeted simulation studies.

Keywords: Model selection; Model averaging; Prediction; Machine learning; Causal inference (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00362-018-1048-3

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