heap: A command for fitting discrete outcome variable models in the presence of heaping at known points

Zizhong Yan (), Wiji Arulampalam, Valentina Corradi and Daniel Gutknecht ()

Stata Journal, 2020, vol. 20, issue 2, 435-467

Abstract: 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 gamma- unobserved heterogeneity, allowing for fixed and individual-specific censoring and different-sized heap points. The second command, heapop, extends the frame- work to ordered choice outcomes, subject to heaping. We also provide suitable specification tests.

Keywords: heapmph; heapop; discrete-time duration model; mixed proportional hazards model; ordered choice model; heaping; measurement error (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:20:y:2019:i:2:p:435-467

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DOI: 10.1177/1536867X20931005

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