Replication data for: Selection Bias and Continuous-Time Duration Models: Consequences and a Proposed Solution

This article analyzes the consequences of nonrandom sample selection for continuous-time duration analyses and develops a new estimator to correct for it when necessary. We conduct a series of Monte Carlo analyses that estimate common duration models as well as our proposed duration model with selection. These simulations show that ignoring sample selection issues can lead to biased parameter estimates, including the appearance of (nonexistent) duration dependence. In addition, our proposed estimator is found to be superior in root mean-square error terms when nontrivial amounts of selection are present. Finally, we provide an empirical application of our method by studying whether self-selectivity is a problem for studies of leaders' survival during and following militarized conflicts.

本文针对非随机样本选择对连续时间持续期分析（continuous-time duration analysis）的影响展开研究，并在必要时提出全新的估计器以修正该类偏误。我们开展了一系列蒙特卡洛（Monte Carlo）分析，分别对常见持续期模型（duration model）与本文提出的带样本选择修正的持续期模型进行估计。仿真结果表明，忽略样本选择问题会导致参数估计结果出现偏误，包括呈现出（本不存在的）持续期依赖性。此外，当存在显著的样本选择效应时，本文提出的估计器在均方根误差（root mean-square error）指标上表现更优。最后，我们通过分析军事化冲突期间及冲突结束后领导人的生存状况，将所提方法应用于实证研究，以此探讨自我选择偏误（self-selectivity）是否为该类领导人生存研究中需要关注的问题。