Model Averaging in Predictive Regressions
Chu-An Liu () and
MPRA Paper from University Library of Munich, Germany
This paper considers 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 propose a frequentist model averaging criterion, an asymptotically unbiased estimator of the mean squared forecast error (MSFE), to select forecast weights. In contrast to the existing literature, we derive the MSFE in a local asymptotic framework without the i.i.d. normal assumption. This result allows us to decompose the MSFE into the bias and variance components and also to account for the correlations between candidate models. Monte Carlo simulations show that our averaging estimator has much lower MSFE than alternative methods such as weighted AIC, weighted BIC, Mallows model averaging, and jackknife model averaging. We apply the proposed method to stock return predictions.
Keywords: Forecast combination; Local asymptotic theory; Plug-in estimators. (search for similar items in EconPapers)
JEL-codes: C52 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/54198/1/MPRA_paper_54198.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/70116/8/MPRA_paper_70116.pdf revised version (application/pdf)
Journal Article: Model averaging in predictive regressions (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:54198
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().