2025 SIMIS-CDEF Seminar Series: No. 4

Talk One:

Beyond First-Order Bias in Predictive Regressions: Estimation, Inference, and Return Predictability

Authors: Liang Jiang, Ron Kaniel, Yayi Yan, Jun Yu
Speaker: Yayi Yan (严雅毅), Shanghai University of Finance and Economics

摘要： This paper exposes significant higher-order bias in predictive regressions and its critical distortion of inferences about return predictability. While conventional methods address first-order bias, we show that higher-order biases, especially for persistent predictors and long predictive horizons in small samples, severely distort both statistical and economic conclusions. To resolve this, we introduce iBoot, a unified framework that combines indirect inference and bootstrap techniques to correct first- and higher-order biases simultaneously in short- and long-horizon regressions. Simulations demonstrate iBoot’s advantages: maximal bias reduction, accurate confidence interval coverage, exact size control, and high power. Applied to canonical stock return predictors, iBoot rejects perceived significance for the dividend-price ratio, book-to-market ratio, and term spread, while confirming the cross-sectional premium as a robust monthly predictor. These findings necessitate addressing higher-order bias in predictive modeling and equipping researchers with a reliable toolkit for disentangling statistical artifacts from genuine economic relationships.