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The paper introduces a new Frequentist model averaging estimation procedure, based on a stacked OLS estimator across models, implementable on cross-sectional, panel, as well as time series data. The proposed estimator shows the same optimal properties of the OLS estimator under the usual set of assumptions concerning the population regression model. Relatively to available alternative approaches, it has the advantage of performing model averaging exante in a single step, optimally selecting models’ weight according to the MSE metric, i.e. by minimizing the squared Euclidean distance between actual and predicted value vectors. Moreover, it is straightforward to implement, only requiring the estimation of a single OLS augmented regression. By exploiting exante a broader information set and benefiting of more degrees of freedom, the proposed approach yields more accurate and relatively more efficient estimation than available expost methods.

KEYWORDS

Model Averaging; Model Uncertainty

Cite this paper

Morana, C. 2015 Model Averaging by Stacking. Open Journal of Statistics, 5, 797-807. doi: 10.4236-ojs.2015.57079.





Autor: Claudio Morana1,2

Fuente: http://www.scirp.org/



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