IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v20y2001i1p105-112.html
   My bibliography  Save this article

Size Characteristics Of Tests For Sample Selection Bias: A Monte Carlo Comparison And Empirical Example

Author

Listed:
  • Kazumitsu Nawata
  • Michael McAleer

Abstract

The t-test of an individual coefficient is used widely in models of qualitative choice. However, it is well known that the t-test can yield misleading results when the sample size is small. This paper provides some experimental evidence on the finite sample properties of the t-test in models with sample selection biases, through a comparison of the t-test with the likelihood ratio and Lagrange multiplier tests, which are asymptotically equivalent to the squared t-test. The finite sample problems with the t-test are shown to be alarming, and much more serious than in models such as binary choice models. An empirical example is also presented to highlight the differences in the calculated test statistics.

Suggested Citation

  • Kazumitsu Nawata & Michael McAleer, 2001. "Size Characteristics Of Tests For Sample Selection Bias: A Monte Carlo Comparison And Empirical Example," Econometric Reviews, Taylor & Francis Journals, vol. 20(1), pages 105-112.
    • Handle: RePEc:taf:emetrv:v:20:y:2001:i:1:p:105-112
    DOI: 10.1081/ETC-100104082
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1081/ETC-100104082
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1081/ETC-100104082?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Belkar, R. & Fiebig, D.G., 2008. "A Monte Carlo comparison of estimators for a bivariate probit model with selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 250-256.
    2. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
    3. Takashi Yamagata & Chris Orme, 2005. "On Testing Sample Selection Bias Under the Multicollinearity Problem," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 467-481.
    4. Rochelle Belkar & Denzil G. Fiebig & Marion Haas & Rosalie Viney, 2006. "Why worry about awareness in choice problems? Econometric analysis of screening for cervical cancer," Health Economics, John Wiley & Sons, Ltd., vol. 15(1), pages 33-47, January.
    5. Dawood Ashraf & Yener Altunbas & John Goddard, 2007. "Who Transfers Credit Risk? Determinants of the Use of Credit Derivatives by Large US Banks," The European Journal of Finance, Taylor & Francis Journals, vol. 13(5), pages 483-500.
    6. repec:ebl:ecbull:v:3:y:2007:i:54:p:1-10 is not listed on IDEAS
    7. Yamagata, Takashi, 2006. "The small sample performance of the Wald test in the sample selection model under the multicollinearity problem," Economics Letters, Elsevier, vol. 93(1), pages 75-81, October.
    8. Maria Ana Odejar & Kostas Mavromaras & Mandy Ryan, 2004. "Messy Data Modelling in Health Care Contingent Valuation Studies," Econometric Society 2004 North American Summer Meetings 406, Econometric Society.
    9. Kazumitsu Nawata, 2007. "A monte carlo analysis of the type II tobit maximum likelihood estimator when the true model is the type I tobit model," Economics Bulletin, AccessEcon, vol. 3(54), pages 1-10.

    More about this item

    Keywords

    Sample selection bias; t-test; Wald test; JEL Classification: C12; C24;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

    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:taf:emetrv:v:20:y:2001:i:1:p:105-112. 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.