Finding 8322Emerging EvidenceValidation V0
This paper introduces a unified empirical likelihood framework for return predictability that stays valid from stationary to mildly explosive predictors, permits conditional heteroskedasticity, and handles growing long-horizon designs. It jointly covers mean and quantile regressions, relaxes intercept restrictions, and outperforms IVX/Bonferroni theory, with simulations and U.S. evidence underscoring practical impact.
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Supporting75% linkage confidence
This paper introduces a unified empirical likelihood framework for return predictability that stays valid from stationary to mildly explosive predictors, permits conditional heteroskedasticity, and handles growing long-horizon designs. It jointly covers mean and quantile regressions, relaxes intercept restrictions, and outperforms IVX/Bonferroni theory, with simulations and U.S. evidence underscoring practical impact.
key_findings bullet 4 · key_findings
Inspect source: Unified Inference for Predictive Mean and Quantile Regressions via Empirical Likelihood →This Finding was extracted from the configured corpus. It is versioned, traceable, and may evolve through editorial review or new corpus evidence.