IDEAS home Printed from https://ideas.repec.org/h/eme/aecozz/s0731-905320160000036022.html
   My bibliography  Save this book chapter

Inference in Near-Singular Regression

In: Essays in Honor of Aman Ullah

Author

Listed:
  • Peter C. B. Phillips

Abstract

This paper considers stationary regression models with near-collinear regressors. Limit theory is developed for regression estimates and test statistics in cases where the signal matrix is nearly singular in finite samples and is asymptotically degenerate. Examples include models that involve evaporating trends in the regressors that arise in conditions such as growth convergence. Structural equation models are also considered and limit theory is derived for the corresponding instrumental variable (IV) estimator, Wald test statistic, and overidentification test when the regressors are endogenous. It is shown that near-singular designs of the type considered here are not completely fatal to least squares inference, but do inevitably involve size distortion except in special Gaussian cases. In the endogenous case, IV estimation is inconsistent and both the block Wald test and Sargan overidentification test are conservative, biasing these tests in favor of the null.

Suggested Citation

  • Peter C. B. Phillips, 2016. "Inference in Near-Singular Regression," Advances in Econometrics, in: Essays in Honor of Aman Ullah, volume 36, pages 461-486, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320160000036022
    DOI: 10.1108/S0731-905320160000036022
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320160000036022/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320160000036022/full/epub?utm_source=repec&utm_medium=feed&utm_campaign=repec&title=10.1108/S0731-905320160000036022
    Download Restriction: no
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320160000036022/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no
    File URL: https://libkey.io/10.1108/S0731-905320160000036022?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
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. M. Hashem Pesaran & Ron P. Smith, 2017. "Posterior Means and Precisions of the Coefficients in Linear Models with Highly Collinear Regressors," CESifo Working Paper Series 6785, CESifo.
    2. Christopher L. Skeels & Frank Windmeijer, 2018. "On the Stock–Yogo Tables," Econometrics, MDPI, vol. 6(4), pages 1-23, November.
    3. Kheifets, Igor L. & Phillips, Peter C.B., 2023. "Fully modified least squares cointegrating parameter estimation in multicointegrated systems," Journal of Econometrics, Elsevier, vol. 232(2), pages 300-319.
    4. Pesaran, M. Hashem & Smith, Ron P., 2019. "A Bayesian analysis of linear regression models with highly collinear regressors," Econometrics and Statistics, Elsevier, vol. 11(C), pages 1-21.
    5. Igor Kheifets & Peter C.B. Phillips, 2019. "Fully Modified Least Squares for Multicointegrated Systems," Cowles Foundation Discussion Papers 2210, Cowles Foundation for Research in Economics, Yale University.
    6. Richard, Patrick, 2019. "Residual bootstrap tests in linear models with many regressors," Journal of Econometrics, Elsevier, vol. 208(2), pages 367-394.

    More about this item

    Keywords

    Endogeneity; instrumental variable; singular signal matrix; size distortion; structural equation; C23;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal 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:eme:aecozz:s0731-905320160000036022. 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: Emerald Support (email available below). General contact details of provider: .

    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.