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Bagging Weak Predictors

Eric Hillebrand (), Manuel Lukas and Wei Wei ()

No 16/20, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Relations between economic variables are often not exploited for forecasting, suggesting that predictors are weak in the sense that the estimation uncertainty is larger than the bias from ignoring the relation. In this paper, we propose a novel bagging estimator designed for such predictors. Based on a test for finite-sample predictive ability, our estimator shrinks the OLS estimate not to zero, but towards the null of the test which equates squared bias with estimation variance, and we apply bagging to further reduce the estimation variance. We derive the asymptotic distribution and show that our estimator can substantially lower the MSE compared to the standard ttest bagging. An asymptotic shrinkage representation for the estimator that simplifies computation is provided. Monte Carlo simulations show that the predictor works well in small samples. In an empirical application, we find that our proposed estimators works well for inflation forecasting using unemployment or industrial production as predictors.

Keywords: inflation forecasting; bootstrap aggregation; estimation uncertainty; weak predictors; shrinkage methods (search for similar items in EconPapers)
JEL-codes: C13 C15 C18 (search for similar items in EconPapers)
Pages: 36
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-gen and nep-ore
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Related works:
Journal Article: Bagging weak predictors (2021) Downloads
Working Paper: Bagging Weak Predictors (2014) Downloads
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