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

Author

Listed:
  • Zizhong Yan

    (Jinan University)

  • Wiji Arulampalam

    (University of Warwick)

  • Valentina Corradi

    (University of Surrey)

  • Daniel Gutknecht

    (Goethe University Frankfurt)

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.

Suggested Citation

  • Zizhong Yan & Wiji Arulampalam & Valentina Corradi & Daniel Gutknecht, 2020. "heap: A command for fitting discrete outcome variable models in the presence of heaping at known points," Stata Journal, StataCorp LP, vol. 20(2), pages 435-467, June.
    • Handle: RePEc:tsj:stataj:v:20:y:2019:i:2:p:435-467
    DOI: 10.1177/1536867X20931005
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