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- W2953285162 abstract "This paper provides an improved inference for predictive quantile regressions with persistent predictors and conditionally heteroskedastic errors. The confidence intervals based on conventional quantile regression techniques are not valid when predictors are highly persistent. Moreover, the conditional heteroskedasticity introduces rather complicated nuisance parameters in the limit theory, whose estimation errors can be another source of distortion. We propose a size-corrected bootstrap inference thereby avoiding the nuisance parameter estimation. The bootstrap consistency is shown even with the nonstationary predictors and conditionally heteroskedastic innovations. Monte Carlo simulation confirms the significantly better test size performances of the new methods. The empirical exercises on stock return quantile predictability are revisited." @default.
- W2953285162 created "2019-06-27" @default.
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- W2953285162 date "2017-01-01" @default.
- W2953285162 modified "2023-09-27" @default.
- W2953285162 title "Predictive Quantile Regressions Under Persistence and Conditional Heteroskedasticity" @default.
- W2953285162 doi "https://doi.org/10.2139/ssrn.3016449" @default.
- W2953285162 hasPublicationYear "2017" @default.
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