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 finite-sample 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 which 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.

CREATES arranges lunch seminars on a regular basis, normally on Tuesdays. The speakers are usually CREATES members, mostly PhD-students and postdocs. The speakers can also be visiting PhD-students or professors.

Each lunch seminar is followed by a discussion of the paper presented.

Organizers: Anders Bredahl Kock and Cristina Amado.

Place: CREATES meeting room, M303, building 2628 (M)

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