Open Journal of Statistics

Volume 5, Issue 7 (December 2015)

ISSN Print: 2161-718X   ISSN Online: 2161-7198

Google-based Impact Factor: 0.53  Citations  

Model Averaging by Stacking

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DOI: 10.4236/ojs.2015.57079    5,133 Downloads   6,265 Views  Citations
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ABSTRACT

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.

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

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