Self-reported survey data are often plagued by the presence of heaping. Accounting for this measurement error is crucial for the identification and consistent estimation of the underlying model (parameters) from such data. In this article, we introduce two commands. The first command, heapmph, estimates the parameters of a discrete-time mixed proportional hazard model with gammaunobserved heterogeneity, allowing for fixed and individual-specific censoring and different-sized heap points. The second command, heapop, extends the framework to ordered choice outcomes, subject to heaping. We also provide suitable specification tests.

中文翻译：

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堆：用于在已知点存在堆的情况下拟合离散结果变量模型的命令

自我报告的调查数据经常受到堆的困扰。考虑到该测量误差对于从此类数据中识别和一致估计基础模型（参数）至关重要。在本文中，我们介绍两个命令。第一个命令heapmph估计具有伽玛未观察到的异质性的离散时间混合比例风险模型的参数，从而允许进行固定的和特定于个人的审查以及不同大小的堆点。第二个命令heapop，将框架扩展到有序选择结果，但要进行堆放。我们还提供合适的规格测试。