Bagging Weak Predictors
Manuel Lukas () and
Eric Hillebrand ()
Additional contact information
Manuel Lukas: Aarhus University and CREATES, Postal: Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Relations between economic variables can often not be exploited for forecasting, suggesting that predictors are weak in the sense that estimation uncertainty is larger than bias from ignoring the relation. In this paper, we propose a novel bagging predictor designed for such weak predictor variables. The predictor is based on a test for finitesample predictive ability. Our predictor shrinks the OLS estimate not to zero, but towards the null of the test which equates squared bias with estimation variance. We derive the asymptotic distribution and show that the predictor can substantially lower the MSE compared to standard t-test bagging. An asymptotic shrinkage representation for the predictor is provided that simplifies computation of the estimator. Monte Carlo simulations show that the predictor works well in small samples. In the empirical application, we find that the new predictor works well for inflation forecasts.
Keywords: Inflation forecasting; bootstrap aggregation; estimation uncertainty; weak predictors (search for similar items in EconPapers)
JEL-codes: C32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Journal Article: Bagging weak predictors (2021)
Working Paper: Bagging Weak Predictors (2020)
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:aah:create:2014-01
Access Statistics for this paper
More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().