IDEAS home Printed from https://ideas.repec.org/p/cdf/wpaper/2011-20.html
   My bibliography  Save this paper

The Robustness of the Higher-Order 2SLS and General k-Class Bias Approximations to Non-Normal Disturbances

Author

Listed:

Abstract

In a seminal paper Nagar (1959) obtained first and second moment approximations for the k-class of estimators in a general static simultaneous equation model under the assumption that the structural disturbances were i.i.d and normally distributed. Later Mikhail (1972) obtained a higher-order bias approximation for 2SLS under the same assumptions as Nagar while Iglesias and Phillips (2010) obtained the higher order approximation for the general k-class of estimators. These approximations show that the higher order biases can be important especially in highly overidentified cases. In this paper we show that Mikhail.s higher order bias approximation for 2SLS continues to be valid under symmetric, but not necessarily normal, disturbances with an arbitrary degree of kurtosis but not when the disturbances are asymmetric. A modified approximation for the 2SLS bias is then obtained which includes the case of asymmetric disturbances. The results are then extended to the general k-class of estimators.

Suggested Citation

  • Phillips, Garry D.A. & Liu-Evans, Gareth, 2011. "The Robustness of the Higher-Order 2SLS and General k-Class Bias Approximations to Non-Normal Disturbances," Cardiff Economics Working Papers E2011/20, Cardiff University, Cardiff Business School, Economics Section.
  • Handle: RePEc:cdf:wpaper:2011/20
    as

    Download full text from publisher

    File URL: http://carbsecon.com/wp/E2011_20.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Emma M. Iglesias & Garry D. A. Phillips, 2012. "Almost Unbiased Estimation in Simultaneous Equation Models With Strong and/or Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 505-520, June.
    2. Phillips, Garry D. A., 2000. "An alternative approach to obtaining Nagar-type moment approximations in simultaneous equation models," Journal of Econometrics, Elsevier, vol. 97(2), pages 345-364, August.
    3. Anderson, T. W. & Kunitomo, Naoto & Morimune, Kimio, 1986. "Comparing Single-Equation Estimators in a Simultaneous Equation System," Econometric Theory, Cambridge University Press, vol. 2(1), pages 1-32, April.
    4. Kinal, Terrence W, 1980. "The Existence of Moments of k-Class Estimators," Econometrica, Econometric Society, vol. 48(1), pages 241-249, January.
    5. Knight, John L., 1985. "The moments of ols and 2sls when the disturbances are non-normal," Journal of Econometrics, Elsevier, vol. 27(1), pages 39-60, January.
    6. Sargan, J D, 1974. "The Validity of Nagar's Expansion for the Moments of Econometric Estimators," Econometrica, Econometric Society, vol. 42(1), pages 169-176, January.
    7. Iglesias, Emma M. & Phillips, Garry D.A., 2010. "The bias to order T-Â 2 for the general k-class estimator in a simultaneous equation model," Economics Letters, Elsevier, vol. 109(1), pages 42-45, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Emma M. Iglesias & Garry D. A. Phillips, 2012. "Almost Unbiased Estimation in Simultaneous Equation Models With Strong and/or Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 505-520, June.
    2. Liu-Evans, Gareth & Phillips, Garry D.A., 2018. "On the use of higher order bias approximations for 2SLS and k-class estimators with non-normal disturbances and many instruments," Econometrics and Statistics, Elsevier, vol. 6(C), pages 90-105.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu-Evans, Gareth & Phillips, Garry D.A., 2018. "On the use of higher order bias approximations for 2SLS and k-class estimators with non-normal disturbances and many instruments," Econometrics and Statistics, Elsevier, vol. 6(C), pages 90-105.
    2. Phillip, Garry & Xu, Yongdeng, 2016. "Almost Unbiased Variance Estimation in Simultaneous Equation Models," Cardiff Economics Working Papers E2016/10, Cardiff University, Cardiff Business School, Economics Section.
    3. Phillips, Garry D.A. & Liu-Evans, Gareth, 2016. "Approximating and reducing bias in 2SLS estimation of dynamic simultaneous equation models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 734-762.
    4. Symeonides Spyridon D. & Karavias Yiannis & Tzavalis Elias, 2017. "Size corrected Significance Tests in Seemingly Unrelated Regressions with Autocorrelated Errors," Journal of Time Series Econometrics, De Gruyter, vol. 9(1), pages 1-41, January.
    5. Emma M. Iglesias & Garry D. A. Phillips, 2012. "Almost Unbiased Estimation in Simultaneous Equation Models With Strong and/or Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 505-520, June.
    6. Wang, Dandan & Phillips, Garry David Alan, 2019. "Bias assessment and reduction for the 2SLS estimator in general dynamic simultaneous equations models," DES - Working Papers. Statistics and Econometrics. WS 28322, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Keisuke Hirano & Jack R. Porter, 2015. "Location Properties of Point Estimators in Linear Instrumental Variables and Related Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 720-733, December.
    8. Liu-Evans, Gareth, 2010. "An alternative approach to approximating the moments of least squares estimators," MPRA Paper 26550, University Library of Munich, Germany.
    9. Liu-Evans, Gareth, 2014. "A note on approximating moments of least squares estimators," MPRA Paper 57543, University Library of Munich, Germany.
    10. Phillips, Garry D. A., 2000. "An alternative approach to obtaining Nagar-type moment approximations in simultaneous equation models," Journal of Econometrics, Elsevier, vol. 97(2), pages 345-364, August.
    11. Stinebrickner Ralph & Stinebrickner Todd R., 2008. "The Causal Effect of Studying on Academic Performance," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 8(1), pages 1-55, June.
    12. repec:ebl:ecbull:v:3:y:2005:i:13:p:1-6 is not listed on IDEAS
    13. Hadri, Kaddour & Phillips, Garry D. A., 1999. "The accuracy of the higher order bias approximation for the 2SLS estimator," Economics Letters, Elsevier, vol. 62(2), pages 167-174, February.
    14. Kaplan, David M. & Sun, Yixiao, 2017. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
    15. Giuseppe Ragusa, 2011. "Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions," Econometric Reviews, Taylor & Francis Journals, vol. 30(4), pages 406-456, August.
    16. Xin Liu, 2019. "Averaging estimation for instrumental variables quantile regression," Papers 1910.04245, arXiv.org.
    17. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
    18. Fernanda Peixe & Alastair Hall & Kostas Kyriakoulis, 2006. "The Mean Squared Error of the Instrumental Variables Estimator When the Disturbance Has an Elliptical Distribution," Econometric Reviews, Taylor & Francis Journals, vol. 25(1), pages 117-138.
    19. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2008. "On Finite Sample Properties of Alternative Estimators of Coefficients in a Structural Equation with Many Instruments," CIRJE F-Series CIRJE-F-577, CIRJE, Faculty of Economics, University of Tokyo.
    20. Patrik Guggenberger, 2006. "Finite-Sample Evidence Suggesting a Heavy Tail Problem of the Generalized Empirical Likelihood Estimator, accepted for publication, Econometric Reviews," UCLA Economics Online Papers 371, UCLA Department of Economics.
    21. Burkhard Raunig, 2019. "Background Indicators," Econometrics, MDPI, Open Access Journal, vol. 7(2), pages 1-14, May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cdf:wpaper:2011/20. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Yongdeng Xu). General contact details of provider: https://edirc.repec.org/data/ecscfuk.html .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.