Model averaging in predictive regressions

Chu-An Liu and Biing‐Shen Kuo

Econometrics Journal, 2016, vol. 19, issue 2, 203-231

Abstract: In this paper, we consider forecast combination in a predictive regression. We construct the point forecast by combining predictions from all possible linear regression models, given a set of potentially relevant predictors. We derive the asymptotic risk of least‐squares averaging estimators in a local asymptotic framework. We then develop a frequentist model averaging criterion, an asymptotically unbiased estimator of the asymptotic risk, to select forecast weights. Monte Carlo simulations show that our averaging estimator compares favourably with alternative methods, such as weighted AIC, weighted BIC, Mallows model averaging and jackknife model averaging. The proposed method is applied to stock return predictions.

Date: 2016
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Citations: View citations in EconPapers (11)

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http://hdl.handle.net/10.1111/ectj.12063

Related works:
Working Paper: Model Averaging in Predictive Regressions (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:wly:emjrnl:v:19:y:2016:i:2:p:203-231

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