Regression quantiles for time series

TLDR: In this paper, a weighted Nadaraya-Watson (WNW) estimator of conditional distribution function was used for regression quantiles for α-mixing time series at both boundary and interior points, and the WNW conditional distribution estimator not only preserves the bias, variance and, more important, automatic good boundary behavior properties of local linear estimators introduced by Yu and Jones (1998, Journal of the American Statistical Association 93, 228-237), but also has the additional advantage of always being a distribution itself.

Abstract: In this paper we study nonparametric estimation of regression quantiles for time series data by inverting a weighted Nadaraya–Watson (WNW) estimator of conditional distribution function, which was first used by Hall, Wolff, and Yao (1999, Journal of the American Statistical Association 94, 154–163). First, under some regularity conditions, we establish the asymptotic normality and weak consistency of the WNW conditional distribution estimator for α-mixing time series at both boundary and interior points, and we show that the WNW conditional distribution estimator not only preserves the bias, variance, and, more important, automatic good boundary behavior properties of local linear “double-kernel” estimators introduced by Yu and Jones (1998, Journal of the American Statistical Association 93, 228–237), but also has the additional advantage of always being a distribution itself. Second, it is shown that under some regularity conditions, the WNW conditional quantile estimator is weakly consistent and normally distributed and that it inherits all good properties from the WNW conditional distribution estimator. A small simulation study is carried out to illustrate the performance of the estimates, and a real example is also used to demonstrate the methodology.