Details

Autor(en) / Beteiligte

• Feng, Yang
• Liu, Qingfeng
• Okui, Ryo

Titel

On the sparsity of Mallows model averaging estimator

Ist Teil von

• Economics letters, 2020-02, Vol.187, p.108916, Article 108916

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• We show that Mallows model averaging estimator proposed by Hansen (2007) can be written as a least squares estimation with a weighted L1 penalty and additional constraints. By exploiting this representation, we demonstrate that the weight vector obtained by this model averaging procedure has a sparsity property in the sense that a subset of models receives exactly zero weights. Moreover, this representation allows us to adapt algorithms developed to efficiently solve minimization problems with many parameters and weighted L1 penalty. In particular, we develop a new coordinate-wise descent algorithm for model averaging. Simulation studies show that the new algorithm computes the model averaging estimator much faster and requires less memory than conventional methods when there are many models. •Mallows Model Averaging has an equivalent constrained weighted Lasso formulation.•The solution of Mallows Model Averaging is sparse in finite samples.•An efficient coordinate-wise descent algorithm was developed for model averaging.