MODEL OF MODELS: A NEW PERSPECTIVE TO DEAL WITH MODEL UNCERTAINTY

This paper presents a novel methodological approach called the Model of Models (MoM). MoM concerns the selection of the best model for a given partition of the data derived from the realization of the independent variables. Compared to ensemble techniques and model averaging approaches proposed in the literature, MoM does not require a selection of which models to include in the pool of models and it works without resorting to the combination of model predictions. MoM works on parametric and non parametric predictive models as well as any other dependent or independent variables. In the case of a partition of the data, the theoretical proposal derives the properties of MoM. The implementation of MoM, when no partition of the data is available in advance, is performed using a new algorithm termed as MoMa. In order to show how MoM works, empirical evidence is provided on simulated data sets. The proved theoretical results coupled with the empirical evidence gathered from simulated data demonstrate that MoM is a good strategy to deal with model choice and model uncertainty.