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A comparison of bias approximations for the two-stage least squares (2SLS) estimator

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  • Bun, Maurice J.G.
  • Windmeijer, Frank

Abstract

We consider the bias of the two-stage least squares (2SLS) estimator in linear instrumental variable regression with only one endogenous regressor. By using asymptotic expansion techniques, we approximate the 2SLS coefficient estimation bias under various scenarios regarding the number and strength of instruments.

Suggested Citation

  • Bun, Maurice J.G. & Windmeijer, Frank, 2011. "A comparison of bias approximations for the two-stage least squares (2SLS) estimator," Economics Letters, Elsevier, vol. 113(1), pages 76-79, October.
    • Handle: RePEc:eee:ecolet:v:113:y:2011:i:1:p:76-79
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    Cited by:

    1. Nam-Hyun Kim & Winfried Pohlmeier, 2015. "A Regularization Approach to Biased Two-Stage Least Squares Estimation," Working Paper series 15-22, Rimini Centre for Economic Analysis.
    2. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.
    3. Chengete Chakamera & Paul Alagidede, 2018. "Electricity crisis and the effect of CO2 emissions on infrastructure-growth nexus in Sub Saharan Africa," Working Papers 731, Economic Research Southern Africa.
    4. Guy Tchuente, 2021. "A Note on the Topology of the First Stage of 2SLS with Many Instruments," Papers 2106.15003, arXiv.org.
    5. Phillips, Garry David Alan & Wang, Dandan, 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.
    6. Manuel Denzer & Constantin Weiser, 2021. "Beyond F-statistic - A General Approach for Assessing Weak Identification," Working Papers 2107, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    7. Millimet, Daniel L., 2015. "Covariate measurement and endogeneity," Economics Letters, Elsevier, vol. 136(C), pages 59-63.
    8. Adam I. Biener & Chad Meyerhoefer & John Cawley, 2024. "Non‐classical measurement error in instrumental variables estimation: An application to the medical care costs of obesity," Health Economics, John Wiley & Sons, Ltd., vol. 33(11), pages 2558-2574, November.
    9. 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.
    10. Namhyun Kim & Winfried Pohlmeier, 2016. "A Note on the Regularized Approach to Biased 2SLS Estimation with Weak Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 915-924, December.
    11. Yiqi Lin & Frank Windmeijer & Xinyuan Song & Qingliang Fan, 2022. "On the instrumental variable estimation with many weak and invalid instruments," Papers 2207.03035, arXiv.org, revised Dec 2023.
    12. Arel-Bundock, Vincent, 2013. "A solution to the weak instrument bias in 2SLS estimation: Indirect inference with stochastic approximation," Economics Letters, Elsevier, vol. 120(3), pages 495-498.
    13. Helmut Farbmacher & Rebecca Groh & Michael Muhlegger & Gabriel Vollert, 2024. "Revisiting the Many Instruments Problem using Random Matrix Theory," Papers 2408.08580, arXiv.org.
    14. Macheret, Dmitry A. (Мачерет, Дмитрий А.) & Valeev, Nadir A. (Валеев, Надир А.) & Kudryavtseva, Anastasiya V. (Кудрявцева, Анастасия В.), 2018. "Formation of the Railway Network: Diffusion of Epochal Innovation and Economic Growth [Формирование Железнодорожной Сети: Диффузия Эпохальной Инновации И Экономический Рост]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 252-279, February.
    15. Akaev, Askar (Акаев, Аскар) & Rudskoy, Andrey (Рудской, Андрей), 2014. "The synergistic effect of NBIC-technologies and world economic growth in the first half of the XXI century [Синергетический Эффект Nbic-Технологий И Мировой Экономический Рост В Первой Половине Xxi," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 2, pages 25-46.

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