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Distribution Regression in Duration Analysis: an Application to Unemployment Spells

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  • Miguel A. Delgado
  • Andr'es Garc'ia-Suaza
  • Pedro H. C. Sant'Anna

Abstract

This article proposes inference procedures for distribution regression models in duration analysis using randomly right-censored data. This generalizes classical duration models by allowing situations where explanatory variables' marginal effects freely vary with duration time. The article discusses applications to testing uniform restrictions on the varying coefficients, inferences on average marginal effects, and others involving conditional distribution estimates. Finite sample properties of the proposed method are studied by means of Monte Carlo experiments. Finally, we apply our proposal to study the effects of unemployment benefits on unemployment duration.

Suggested Citation

  • Miguel A. Delgado & Andr'es Garc'ia-Suaza & Pedro H. C. Sant'Anna, 2019. "Distribution Regression in Duration Analysis: an Application to Unemployment Spells," Papers 1904.06185, arXiv.org, revised Nov 2021.
    • Handle: RePEc:arx:papers:1904.06185
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    Cited by:

    1. Santiago Acerenza & Vitor Possebom & Pedro H. C. Sant'Anna, 2023. "Was Javert right to be suspicious? Unpacking treatment effect heterogeneity of alternative sentences on time-to-recidivism in Brazil," Papers 2311.13969, arXiv.org, revised Jan 2025.
    2. Chen, Songnian, 2023. "Two-step estimation of censored quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 235(2), pages 1310-1336.
    3. Yao, Li & Li, Jun & Chen, Kaihua & Yu, Rongjian, 2024. "Winning the second race of technology standardization: Strategic maneuvers in SEP follow-on innovations," Research Policy, Elsevier, vol. 53(6).

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