| dc.contributor | 國貿系 | |
| dc.creator (作者) | 郭炳伸 | zh-tw |
| dc.creator (作者) | Liu, Chu-An;Kuo, Biing-Shen | en-US |
| dc.date (日期) | 2016-06 | |
| dc.date.accessioned | 12-Jul-2017 11:06:22 (UTC+8) | - |
| dc.date.available | 12-Jul-2017 11:06:22 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Jul-2017 11:06:22 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/110942 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 593712 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Econometrics Journal, 19(2), 203-231 | |
| dc.subject (關鍵詞) | Forecast combination; Local asymptotic theory; Plug-in estimators | |
| dc.title (題名) | Model Averaging in Predictive Regressions | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1111/ectj.12063 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1111/ectj.12063 | |
