Clive Granger proposed thick modelling as an alternative to selecting a unique model based on a given criterion, or thin modelling. This stemmed from his research on forecast combination and portfolio selection in which using just the best asset or forecast can be suboptimal in many settings. This paper proposes to integrate thick modelling into the general-to-specific model selection literature, yielding the benefits of selecting a set of well-specified encompassing models while taking seriously Granger’s critique of model selection. The paper argues that model uncertainty is addressed by applying selection to narrow down the class of models followed by pooling across the retained set of close specifications. An example using artificial data illustrates the approach.


Castle, J.L. (2017). 'Sir Clive W.J. Granger Model Selection', 10(1), 133-156.
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