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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/110942


    Title: Model Averaging in Predictive Regressions
    Authors: 郭炳伸
    Liu, Chu-An;Kuo, Biing-Shen
    Contributors: 國貿系
    Keywords: Forecast combination;Local asymptotic theory;Plug-in estimators
    Date: 2016-06
    Issue Date: 2017-07-12 11:06:22 (UTC+8)
    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.
    Relation: Econometrics Journal, 19(2), 203-231
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1111/ectj.12063
    DOI: 10.1111/ectj.12063
    Appears in Collections:[國際經營與貿易學系 ] 期刊論文

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