IDEAS home Printed from https://ideas.repec.org/a/oup/restud/v77y2010i1p127-153.html
   My bibliography  Save this article

Non-Parametric Identification and Estimation of Truncated Regression Models

Author

Listed:
  • Songnian Chen

Abstract

In this paper, we consider non-parametric identification and estimation of truncated regression models in both cross-sectional and panel data settings. For the cross-sectional case, Lewbel and Linton (2002) considered non-parametric identification and estimation through continuous variation under a log-concavity condition on the error distribution. We obtain non-parametric identification under weaker conditions. In particular, we obtain non-parametric identification through discrete variation under a non-periodicity condition on the hazard function of the error distribution. Furthermore, we show that the presence of continuous regressors may lead to stronger identification results. Our non-parametric estimator is shown to be consistent and asymptotically normal, and outperforms that of Lewbel and Linton (2002) in a simulation study. For the panel data setting, we provide the first systematic treatment of non-parametric identification and estimation of the truncated panel data model with fixed effects by extending our treatment of the cross-sectional case. We also consider various other extensions. Copyright , Wiley-Blackwell.

Suggested Citation

  • Songnian Chen, 2010. "Non-Parametric Identification and Estimation of Truncated Regression Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(1), pages 127-153.
    • Handle: RePEc:oup:restud:v:77:y:2010:i:1:p:127-153
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1467-937X.2009.00572.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    2. Joan Costa-Font & Sergi Jiménez-Martín & Cristina Villaplana, 2016. "Does Long-Term Care Subsidisation Reduce Unnecessary Hospitalisations?," Working Papers 2016-05, FEDEA.
    3. Pierre Dubois & Olivier de Mouzon & Fiona Scott-Morton & Paul Seabright, 2015. "Market size and pharmaceutical innovation," RAND Journal of Economics, RAND Corporation, vol. 46(4), pages 844-871, October.
    4. Joan Costa-i-Font & Sergi Jimenez-Martin & Cristina Vilaplana, 2016. "Does Long-Term Care Subsidisation Reduce Hospital Admissions?," CESifo Working Paper Series 6078, CESifo.
    5. Kevin E. Staub, 2014. "A Causal Interpretation of Extensive and Intensive Margin Effects in Generalized Tobit Models," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 371-375, May.
    6. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," Department of Economics Working Papers 2020-09, McMaster University.

    More about this item

    Statistics

    Access and download statistics

    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:restud:v:77:y:2010:i:1:p:127-153. 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.

    We have no bibliographic references for this item. You can help adding them by using 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://academic.oup.com/restud .

    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.