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Multiple Regression Model Averaging and the Focused Information Criterion With an Application to Portfolio Choice

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  • Filip Klimenka
  • James Lewis Wolter

Abstract

We consider multiple regression (MR) model averaging using the focused information criterion (FIC). Our approach is motivated by the problem of implementing a mean-variance portfolio choice rule. The usual approach is to estimate parameters ignoring the intention to use them in portfolio choice. We develop an estimation method that focuses on the trading rule of interest. Asymptotic distributions of submodel estimators in the MR case are derived using a localization framework. The localization is of both regression coefficients and error covariances. Distributions of submodel estimators are used for model selection with the FIC. This allows comparison of submodels using the risk of portfolio rule estimators. FIC model averaging estimators are then characterized. This extension further improves risk properties. We show in simulations that applying these methods in the portfolio choice case results in improved estimates compared with several competitors. An application to futures data shows superior performance as well.

Suggested Citation

  • Filip Klimenka & James Lewis Wolter, 2019. "Multiple Regression Model Averaging and the Focused Information Criterion With an Application to Portfolio Choice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 506-516, July.
    • Handle: RePEc:taf:jnlbes:v:37:y:2019:i:3:p:506-516
    DOI: 10.1080/07350015.2017.1383262
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    Cited by:

    1. Liao, Jun & Wan, Alan T.K. & He, Shuyuan & Zou, Guohua, 2022. "Optimal model averaging for multivariate regression models," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

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