Multiple Regression Model Averaging and the Focused Information Criterion With an Application to Portfolio Choice
针对均值-方差投资组合选择问题，提出基于聚焦信息准则的多元回归模型平均估计方法，利用局部化框架推导子模型估计量的渐近分布，模拟和期货数据实证显示该方法优于传统估计。
We consider multiple regression (MR) model averaging using the Focused Informati on 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.