TY - JOUR
T1 - Bagging weak predictors
AU - Hillebrand, Eric
AU - Lukas, Manuel
AU - Wei, Wei
N1 - Funding Information:
The authors acknowledge support from CREATES - Center for Research in Econometric Analysis of Time Series , funded by the Danish National Research Foundation ( DNRF78 ). The views and opinions expressed in this article are those of the authors alone and do not necessarily reflect the official policy or position of Swiss Life Asset Managers.
Publisher Copyright:
© 2020 International Institute of Forecasters
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021/1
Y1 - 2021/1
N2 - Often, relations between economic variables cannot be 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 ordinary least squares estimate—not to zero, but towards the null of the test that equates squared bias with estimation variance. We apply bagging to reduce the estimation variance further. We derive the asymptotic distribution and show that our estimator substantially lowers the mean-squared error compared to standard t-test bagging. An asymptotic shrinkage representation for the estimator that simplifies the computation is provided. Monte Carlo simulations showed that the predictor works well with small samples. Empirically, we found that our proposed estimator worked well for inflation forecasting when using unemployment or industrial production as predictors. In an application for predicting equity premiums, the combination of our estimator and a positive constraint on forecasts delivered statistically significant gains relative to the historical average using a wide range of predictors.
AB - Often, relations between economic variables cannot be 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 ordinary least squares estimate—not to zero, but towards the null of the test that equates squared bias with estimation variance. We apply bagging to reduce the estimation variance further. We derive the asymptotic distribution and show that our estimator substantially lowers the mean-squared error compared to standard t-test bagging. An asymptotic shrinkage representation for the estimator that simplifies the computation is provided. Monte Carlo simulations showed that the predictor works well with small samples. Empirically, we found that our proposed estimator worked well for inflation forecasting when using unemployment or industrial production as predictors. In an application for predicting equity premiums, the combination of our estimator and a positive constraint on forecasts delivered statistically significant gains relative to the historical average using a wide range of predictors.
KW - Bootstrap aggregation
KW - Equity premium predictions
KW - Estimation uncertainty
KW - Inflation forecasting
KW - Shrinkage methods
KW - Weak predictors
UR - http://www.scopus.com/inward/record.url?scp=85087210393&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2020.05.002
DO - 10.1016/j.ijforecast.2020.05.002
M3 - Journal article
AN - SCOPUS:85087210393
SN - 0169-2070
VL - 37
SP - 237
EP - 254
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 1
ER -