IDEAS home Printed from https://ideas.repec.org/a/oup/emjrnl/v25y2022i3p675-698..html
   My bibliography  Save this article

Distribution regression in duration analysis: an application to unemployment spells
[Lecture notes in statistics: Proceedings]

Author

Listed:
  • Miguel A Delgado
  • Andrés García-Suaza
  • Pedro H C Sant’Anna

Abstract

SummaryThis 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 & Andrés García-Suaza & Pedro H C Sant’Anna, 2022. "Distribution regression in duration analysis: an application to unemployment spells [Lecture notes in statistics: Proceedings]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 675-698.
    • Handle: RePEc:oup:emjrnl:v:25:y:2022:i:3:p:675-698.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ectj/utac007
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Wang, Huixia Judy & Wang, Lan, 2009. "Locally Weighted Censored Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1117-1128.
    2. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    3. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    4. Gerard J. van den Berg, 1990. "Nonstationarity in Job Search Theory," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(2), pages 255-277.
    5. Thomas H. Scheike & Mei-Jie Zhang & Thomas A. Gerds, 2008. "Predicting cumulative incidence probability by direct binomial regression," Biometrika, Biometrika Trust, vol. 95(1), pages 205-220.
    6. Raj Chetty, 2008. "Moral Hazard versus Liquidity and Optimal Unemployment Insurance," Journal of Political Economy, University of Chicago Press, vol. 116(2), pages 173-234, April.
    7. Honore, Bo & Khan, Shakeeb & Powell, James L., 2002. "Quantile regression under random censoring," Journal of Econometrics, Elsevier, vol. 109(1), pages 67-105, July.
    8. Victor Chernozhukov & Iv'an Fern'andez-Val & Siyi Luo, 2018. "Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK," Papers 1811.11603, arXiv.org, revised Dec 2023.
    9. Portnoy S., 2003. "Censored Regression Quantiles," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1001-1012, January.
    10. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
    11. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "Hypothesis Testing in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 75-104, September.
    12. Krueger, Alan B. & Meyer, Bruce D., 2002. "Labor supply effects of social insurance," Handbook of Public Economics, in: A. J. Auerbach & M. Feldstein (ed.), Handbook of Public Economics, edition 1, volume 4, chapter 33, pages 2327-2392, Elsevier.
    13. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    14. Raj Chetty, 2008. "Moral Hazard versus Liquidity and Optimal Unemployment Insurance," Journal of Political Economy, University of Chicago Press, vol. 116(2), pages 173-234, April.
    15. Torsten Hothorn & Thomas Kneib & Peter Bühlmann, 2014. "Conditional transformation models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 3-27, January.
    16. David Hunter & Kenneth Lange, 2002. "Computing Estimates in the Proportional Odds Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(1), pages 155-168, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaofeng Lv & Gupeng Zhang & Xinkuo Xu & Qinghai Li, 2019. "Weighted quantile regression for censored data with application to export duration data," Statistical Papers, Springer, vol. 60(4), pages 1161-1192, August.
    2. Konstantinos Tatsiramos & Jan C. Ours, 2014. "Labor Market Effects Of Unemployment Insurance Design," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 284-311, April.
    3. Rafael Lalive & Camille Landais & Josef Zweimüller, 2015. "Market Externalities of Large Unemployment Insurance Extension Programs," American Economic Review, American Economic Association, vol. 105(12), pages 3564-3596, December.
    4. Lin, Guixian & He, Xuming & Portnoy, Stephen, 2012. "Quantile regression with doubly censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 797-812.
    5. Tito Boeri & Jan van Ours, 2013. "The Economics of Imperfect Labor Markets: Second Edition," Economics Books, Princeton University Press, edition 1, number 10142.
    6. Peng, Limin, 2012. "Self-consistent estimation of censored quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 368-379.
    7. Möst Lisa & Hothorn Torsten, 2015. "Conditional Transformation Models for Survivor Function Estimation," The International Journal of Biostatistics, De Gruyter, vol. 11(1), pages 23-50, May.
    8. Stefan Groot & Henri de Groot, 2011. "Wage inequality in the Netherlands: Evidence, trends and explanations," CPB Discussion Paper 186.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    9. Xie, Shangyu & Wan, Alan T.K. & Zhou, Yong, 2015. "Quantile regression methods with varying-coefficient models for censored data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 154-172.
    10. Pang, Lei & Lu, Wenbin & Wang, Huixia Judy, 2012. "Variance estimation in censored quantile regression via induced smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 785-796.
    11. 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.
    12. Egbert Jongen, 2009. "An analysis of individual accounts for the unemployment risk in the Netherlands," CPB Document 186.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    13. Fan, Yanqin & Liu, Ruixuan, 2018. "Partial identification and inference in censored quantile regression," Journal of Econometrics, Elsevier, vol. 206(1), pages 1-38.
    14. Egbert Jongen, 2009. "An analysis of individual accounts for the unemployment risk in the Netherlands," CPB Document 186, CPB Netherlands Bureau for Economic Policy Analysis.
    15. García, A., 2016. "Oaxaca-Blinder Type Counterfactual Decomposition Methods for Duration Outcomes," Documentos de Trabajo 14186, Universidad del Rosario.
    16. Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2022. "Instrumental variable quantile regression under random right censoring," Papers 2209.01429, arXiv.org, revised Feb 2023.
    17. Narisetty, Naveen & Koenker, Roger, 2022. "Censored quantile regression survival models with a cure proportion," Journal of Econometrics, Elsevier, vol. 226(1), pages 192-203.
    18. Fevang, Elisabeth & Hardoy, Inés & Røed, Knut, 2013. "Getting Disabled Workers Back to Work: How Important Are Economic Incentives?," IZA Discussion Papers 7137, Institute of Labor Economics (IZA).
    19. Gerards, Ruud & Welters, Ricardo, 2016. "Impact of financial pressure on unemployed job search, job find success and job quality," ROA Research Memorandum 008, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    20. Camille Landais & Pascal Michaillat & Emmanuel Saez, 2018. "A Macroeconomic Approach to Optimal Unemployment Insurance: Applications," American Economic Journal: Economic Policy, American Economic Association, vol. 10(2), pages 182-216, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:emjrnl:v:25:y:2022:i:3:p:675-698.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/resssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.