IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/y16y2016i2p368-385.html
   My bibliography  Save this article

Simpler standard errors for two-stage optimization estimators estimation in normal linear models

Author

Listed:
  • Joseph V. Terza

    (Indiana University Purdue University Indianapolis)

Abstract

Aiming to lessen the analytic and computational burden faced by practitioners seeking to correct the standard errors of two-stage estimators, I offer a heretofore unexploited simplification of the conventional formulation for the most commonly encountered cases in empirical application—two-stage estimators that involve maximum likelihood or pseudomaximum likelihood estimation. With the applied researcher in mind, I focus on the two-stage residual inclusion estimator designed for nonlinear regression models involving endogeneity. I demonstrate the analytics and Stata and Mata code for implementing my simplified standard-error formula by applying the two-stage residual inclusion method to the birthweight model of Mullahy (1997, Review of Economics and Statistics 79: 586–593) using his original data. Copyright 2016 by StataCorp LP.

Suggested Citation

  • Joseph V. Terza, 2016. "Simpler standard errors for two-stage optimization estimators estimation in normal linear models," Stata Journal, StataCorp LP, vol. 16(2), pages 368-385, June.
  • Handle: RePEc:tsj:stataj:y:16:y:2016:i:2:p:368-385
    as

    Download full text from publisher

    File URL: http://www.stata-journal.com/article.html?article=st0436
    File Function: link to article purchase
    Download Restriction: no

    File URL: http://www.stata-journal.com/software/sj16-2/st0436/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Sergi Jiménez-Martín & José M. Labeaga & Majid al Sadoon, 2020. "Consistent estimation of panel data sample selection models," Working Papers 2020-06, FEDEA.
    2. Anne‐Célia Disdier & Carl Gaigné & Cristina Herghelegiu, 2023. "Do standards improve the quality of traded products?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 56(4), pages 1238-1290, November.
    3. Wendkouni Jean‐Baptiste Zongo & Bruno Larue & Carl Gaigné, 2023. "On export duration puzzles," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(2), pages 453-478, March.
    4. Geraci Andrea & Fabbri Daniele & Monfardini Chiara, 2018. "Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?," Journal of Econometric Methods, De Gruyter, vol. 7(1), pages 1-19, January.

    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:tsj:stataj:y:16:y:2016:i:2:p:368-385. 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: Christopher F. Baum or Lisa Gilmore (email available below). General contact details of provider: http://www.stata-journal.com/ .

    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.