IDEAS home Printed from https://ideas.repec.org/a/cup/etheor/v16y2000i04p551-575_16.html
   My bibliography  Save this article

Semiparametric Estimation Of Multiple Equation Models

Author

Listed:
  • Picone, Gabriel A.
  • Butler, J.S.

Abstract

This paper proposes a semiparametric estimator for multiple equations multiple index (MEMI) models. Examples of MEMI models include several sample selection models and the multinomial choice model. The proposed estimator minimizes the average distance between the dependent variable unconditional and conditional on an index. The estimator is √N-consistent and asymptotically normally distributed. The paper also provides a Monte Carlo experiment to evaluate the finite-sample performance of the estimator.

Suggested Citation

  • Picone, Gabriel A. & Butler, J.S., 2000. "Semiparametric Estimation Of Multiple Equation Models," Econometric Theory, Cambridge University Press, vol. 16(4), pages 551-575, August.
  • Handle: RePEc:cup:etheor:v:16:y:2000:i:04:p:551-575_16
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0266466600164047/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Donkers, A.C.D. & Schafgans, M., 2003. "A Derivative Based Estimator for Semiparametric Index Models," Other publications TiSEM 92ffa14b-de76-4309-8bee-1, Tilburg University, School of Economics and Management.
    2. Lee, Myoung-jae & Kim, Young-sook, 2007. "Multinomial choice and nonparametric average derivatives," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 63-81, January.
    3. Michel Delecroix & Marian Hristache & Valentin Patilea, 2004. "On Semiparametric estimation in Single-Index Regression," Working Papers 2004-17, Center for Research in Economics and Statistics.
    4. Jérôme Foncel & Marian Hristache & Valentin Patilea, 2004. "Semiparametric Single-index Poisson Regression Model with Unobserved Heterogeneity," Working Papers 2004-04, Center for Research in Economics and Statistics.
    5. Donkers, Bas & Schafgans, Marcia M. A., 2005. "A method of moments estimator for semiparametric index models," LSE Research Online Documents on Economics 6815, London School of Economics and Political Science, LSE Library.
    6. Jeff Racine, 2002. "Generalized Semiparametric Binary Prediction," Annals of Economics and Finance, Society for AEF, vol. 3(1), pages 117-134, May.

    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:cup:etheor:v:16:y:2000:i:04:p:551-575_16. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/ect .

    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.