Two-step estimation of censored quantile regression for duration models with time-varying regressors
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含时变解释变量的持续时间模型：删失分位数回归两步估计
- 研究问题
- 持续时间数据中，如何允许解释变量对不同条件分位数产生异质影响，并降低时变解释变量与删失共同造成的估计计算负担？
- 方法与思路
- 作者沿用时变解释变量的持续时间分位数回归框架，提出易于实施的两步估计，缓解原估计中的非凸、非线性高维优化困难。
- 材料陈述的贡献
- 摘要称，两步估计显著降低计算负担，具有一致性和渐近正态性；蒙特卡洛实验显示其在所考察有限样本中表现良好。
- 阅读关注点
- 应核对两步估计各自的目标与输入、删失处理及相应条件，时变变量如何进入分位数函数，以及计算改善的实际比较方式。
- 阅读边界
- 仅据所给RePEc出版社摘要，不能推断刊本变化；摘要未说明删失机制、完整识别条件、算法细节或具体计算和有限样本结果。
原文摘要
Common duration models are characterized by strong homogeneity and thus are highly restrictive in allowing for how regressors affect the conditional duration distribution. In particular, the implied sign and relative marginal quantile effects remain the same over the entire range of the conditional duration distribution, which rules out general heterogeneous effects in duration data. Quantile regression, which offers a flexible and unified framework that allows for general heterogeneous effects, is particularly well suited to duration analysis. Based on the insights behind the accelerated failure time model (AFT) with time-varying regressors (Cox and Oakes, 1984) and the standard quantile regression model (Koenker and Bassett, 1978), Chen (2019) recently developed a quantile regression framework with time-varying regressors. However, Chen’s (2019) estimator is very difficult to compute because the estimation procedure involves a non-convex and nonlinear high dimensional optimization problem due to censoring and the nonlinearity of the quantile function. In this paper I propose an easy-to-implement two-step quantile regression estimator, which significantly reduces the computational burden. The estimator is shown to be consistent and asymptotically normal. Monte Carlo experiments indicate that our estimator perform well in finite samples.