Alex Maynard, Katsumi Shimotsu, Nina Kuriyama
arXiv 1 Jun 2023 · Econometrics · publishedJournal of Econometrics (2024) · 14 citations (OpenAlex)
arXiv:2306.00296 · PDF · DOI · OpenAlex · Extracted main text
This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t-statistic in terms of functionals of Ornstein-Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has a reliable size in small samples and good power. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors - the dividend price ratio, earnings price ratio, and book-to-market ratio - to predict the median, shoulders, and tails of the stock return distribution.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cai, Z., Chen, H., and Liao, X (2023) A New Robust Inference for Predictive Quantile Regression | 1.000 | 18 | 5 | 100% |
| 2 | Maynard, A., Shimotsu, K., and Wang, Y (2011) Inference in Predictive Quantile Regressions self | 1.000 | 8 | 5 | 100% |
| 3 | Fan, R. and Lee, J. H (2019) Predictive Quantile Regressions under Persistence and Conditional Heteroskedasticity | 1.000 | 6 | 3 | 100% |
| 4 | Lee, J. H (2016) Predictive Quantile Regression with Persistent Covariates: IVX-QR Approach | 0.976 | 14 | 7 | 93% |
| 5 | Stock, J. H (1991) Confidence intervals for the largest autoregressive root in U.S. economic time series | 0.928 | 4 | 3 | 100% |
| 6 | Phillips, P. C. B (2014) On Confidence Intervals for Autoregressive Roots and Predictive Regression | 0.888 | 10 | 6 | 70% |
| 7 | Campbell, J. Y. and Yogo, M (2006) Efficient tests of stock return predictability | 0.874 | 10 | 2 | 100% |
| 8 | Goyal, A. and Welch, I (2008) A Comprehensive Look at the Empirical Performance of Equity Premium Prediction | 0.874 | 6 | 2 | 100% |
| 9 | Elliott, G., Muller, U. K., and Watson, M (2015) Nearly Optimal Tests when a Nuisance Parameter is Present Under the Null Hypothesis | 0.874 | 5 | 2 | 100% |
| 10 | Cenesizoglu, T. and Timmermann, A (2008) Is the Distribution of Stock Returns Predictable? | 0.843 | 3 | 3 | 100% |
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