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Luiz Renato Lima
;
Fanning Meng

out-of-sample return predictability: a quantile combination approach (replication data)

This paper develops a novel forecasting method that minimizes the effects of weak predictors and estimation errors on the accuracy of equity premium forecasts. The proposed method is based on an averaging scheme applied to quantiles conditional on predictors selected by LASSO. The resulting forecasts outperform the historical average, and other existing models, by statistically and economically meaningful margins.

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Lima, Luiz Renato; Meng, Fanning (2017): Out-of-Sample Return Predictability: A Quantile Combination Approach (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022326.0703491673

Related Publication

Lima, L. and Meng, F. (2017), Out-Of-Sample Return Predictability: A Quantile Combination Approach, Journal of Applied Econometrics, 32(4), 877-895. https://doi.org/10.1002/jae.2549

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