Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series
时间序列分位数与预期短缺回归中的自归一化推断
This paper proposes valid inference tools, based on self-normalization, in time series expected shortfall regressions and, as a corollary, also in quantile regressions. Extant methods for such time series regressions, based on a bootstrap or direct estimation of the long-run variance, are computationally more involved, require the choice of tuning parameters and have serious size distortions when the regression errors are strongly serially dependent. In contrast, our…