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Bagging Weak Predictors

Manuel Lukas () and Eric Hillebrand ()
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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

Abstract: 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)
Pages: 36
Date: 2014-01-07
New Economics Papers: this item is included in nep-ecm
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ftp://ftp.econ.au.dk/creates/rp/14/rp14_01.pdf (application/pdf)

Related works:
Journal Article: Bagging weak predictors (2021) Downloads
Working Paper: Bagging Weak Predictors (2020) Downloads
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