Testing the proportional hazards assumption in the presence of unmeasured heterogeneity
提出一个离散时间风险模型，允许回归系数随时间变化，并推导了未观测异质性分布的非参数可识别条件。该模型可用于检验连续时间风险模型的比例性假设，并通过蒙特卡洛模拟和失业工人数据示例展示了其小样本性质。
Abstract This paper develops a discrete‐time hazard model which accounts for unmeasured hetergeneity while allowing the coefficients of the regressors to vary over time. Sufficient conditions for nonparametric identifiability of the unmeasured heterogeneity distribution are derived. Testing for time‐varying coefficients in this model is, under suitable conditions, equivalent to testing the proportionality assumption of the underlying continuous‐time hazard model. Some Monte Carlo evidence is presented regarding the small‐sample properties of this test. As an illustration, these tests are applied to the joblessness durations of displaced workers.