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Quantile Autoregression for Censored Data
Author(s) -
Choi Seokwoo Jake,
Portnoy Stephen
Publication year - 2016
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12174
Subject(s) - censoring (clinical trials) , quantile , quantile regression , estimator , autoregressive model , econometrics , mathematics , statistics , monte carlo method , imputation (statistics) , missing data
Quantile autoregression (QAR) is particularly attractive for censored data. However, unlike the standard regression models, the autoregressive models must take account of censoring on both response and regressors. In this article, we show that the existing censored quantile regression methods produce consistent estimators for QAR models when using only the fully observed regressors. A new algorithm is proposed to provide a censored QAR estimator by adopting imputation methods. The algorithm redistributes probability mass of censored points appropriately and iterates towards self‐consistent solutions. Monte Carlo simulations and empirical applications are conducted to demonstrate merits of the proposed method.