  The Multi-Kink Quantile Regression (MKQR) model is an important tool for
analyzing data with heterogeneous conditional distributions, especially when
quantiles of response variable are of interest, due to its robustness to
outliers and heavy-tailed errors in the response. It assumes different linear
quantile regression forms in different regions of the domain of the threshold
covariate but are still continuous at kink points. In this paper, we
investigate parameter estimation, kink point detection and statistical
inference in MKQR models. We propose an iterative segmented quantile regression
algorithm for estimating both the regression coefficients and the locations of
kink points. The proposed algorithm is much more computationally efficient than
the grid search algorithm and not sensitive to the selection of initial values.
Asymptotic properties, such as selection consistency of the number of kink
points, asymptotic normality of the estimators of both regression coefficients
and kink effects, are established to justify the proposed method theoretically.
A score test, based on partial subgradients, is developed to verify whether the
kink effects exist or not. Test-inversion confidence intervals for kink
location parameters are also constructed. Intensive simulation studies
conducted show the proposed methods work very well when sample size is finite.
Finally, we apply the MKQR models together with the proposed methods to the
dataset about secondary industrial structure of China and the dataset about
triceps skinfold thickness of Gambian females, which leads to some very
interesting findings. A new R package MultiKink is developed to implement the
proposed methods.
