Linear instrumental variables model averaging estimation
Martins, LF and Gabriel, VJ (2014) Linear instrumental variables model averaging estimation Computational Statistics and Data Analysis, 71. pp. 709-724.
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Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. The obtaining of weights for averaging across individual estimates by direct smoothing of selection criteria arising from the estimation stage is proposed. This is particularly relevant in applications in which there is a large number of candidate instruments and, therefore, a considerable number of instrument sets arising from different combinations of the available instruments. The asymptotic properties of the estimator are derived under homoskedastic and heteroskedastic errors. A simple Monte Carlo study contrasts the performance of MA procedures with existing instrument selection procedures, showing that MA estimators compare very favorably in many relevant setups. Finally, this method is illustrated with an empirical application to returns to education. © 2013 Elsevier B.V. All rights reserved.
|Divisions :||Faculty of Arts and Social Sciences > School of Economics|
|Date :||March 2014|
|Identification Number :||10.1016/j.csda.2013.05.008|
|Additional Information :||NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis, 71, March 2014, DOI 10.1016/j.csda.2013.05.008.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||16 Jan 2015 10:56|
|Last Modified :||16 Jan 2015 10:56|
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