Quantile autoregression with censored data

Censored time-series;Autoregression;Quantile;Self-consistent;Kaplan-Meier

Choi, Seokwoo

关键词: Censored time-series;    Autoregression;    Quantile;    Self-consistent;    Kaplan-Meier;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/50583/Seokwoo_Choi.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Quantile autoregression (QAR) provides an alternative way to study asymmetric dynamics and local persistence in time series. It is particularly attractive for censored data, where the classical autoregressive models are unidentifiable without further parametric assumptions on the distributions. There have been prominent works by Powell (1986), Portnoy (2003) and Peng and Huang (2008) on estimating the conditional quantile functions with censored data. However, unlike the standard regression models, the autoregressive models should take account of censoring on both response and regressors.In this dissertation, we show that the existing censored quantile regression methods produce empirically consistent estimator onQAR models when using only observedpart of regressors. A new algorithm is proposed to improve a censored quantile autoregression (CQAR) estimator by adopting an idea of imputation methods. The algorithm distributes probability mass of each censored point to any sufficiently large value appropriately, and iterates towards self-consistent solutions.Monte Carlo simulations are conducted to examine the empirical consistency of the CQAR estimator. Also, empirical applications of the algorithm to the Samish river water quality study and dry decomposition of NH4 demonstrate the merits of the proposed method.