Model Averaging by Stacking

Claudio Morana

No 310, Working Papers from University of Milano-Bicocca, Department of Economics

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 ex-ante a broader information set and benefiting of more degrees of freedom, the proposed approach yields more accurate and (relatively) more efficient estimation than available ex-post methods.

Keywords: Model Averaging; Model Uncertainty (search for similar items in EconPapers)
JEL-codes: C30 C51 (search for similar items in EconPapers)
Pages: 14
Date: 2015-10-29, Revised 2015-10-29
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (6)

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Working Paper: Model Averaging by Stacking (2015)
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