Predictive Regression and Robust Hypothesis Testing: Predictability Hidden by Anomalous Observations

OMI Seminar Series

Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly

integrated regressors and applicable to multi-predictor settings, when the data may only approximately follow a predictive regression model. The Monte Carlo evidence demonstrates large improvements of our approach, while the empirical analysis produces a strong robust evidence of market return predictability, using predictive variables such as the dividend yield, the volatility risk premium or, labor income.


Fabio Trojani (Swiss Finance Institute)

Tuesday, February 5, 2013 - 14:15
to 15:15