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Constructing Optimal Instruments by First-Stage Prediction Averaging

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  • Guido Kuersteiner
  • Ryo Okui

Abstract

This paper considers model averaging as a way to construct optimal instruments for the two-stage least squares (2SLS), limited information maximum likelihood (LIML), and Fuller estimators in the presence of many instruments. We propose averaging across least squares predictions of the endogenous variables obtained from many different choices of instruments and then use the average predicted value of the endogenous variables in the estimation stage. The weights for averaging are chosen to minimize the asymptotic mean squared error of the model averaging version of the 2SLS, LIML, or Fuller estimator. This can be done by solving a standard quadratic programming problem. Copyright 2010 The Econometric Society.

Suggested Citation

  • Guido Kuersteiner & Ryo Okui, 2010. "Constructing Optimal Instruments by First-Stage Prediction Averaging," Econometrica, Econometric Society, vol. 78(2), pages 697-718, March.
  • Handle: RePEc:ecm:emetrp:v:78:y:2010:i:2:p:697-718
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    References listed on IDEAS

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    Cited by:

    1. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, Open Access Journal, vol. 1(2), pages 1-23, September.
    2. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, pages 187-208.
    3. Aman Ullah & Xinyu Zhang, 2015. "Grouped Model Averaging for Finite Sample Size," Working Papers 201501, University of California at Riverside, Department of Economics.
    4. Caner, Mehmet & Fan, Qingliang, 2015. "Hybrid generalized empirical likelihood estimators: Instrument selection with adaptive lasso," Journal of Econometrics, Elsevier, pages 256-274.
    5. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, pages 443-464.
    6. Daron Acemoglu & Ufuk Akcigit & Douglas Hanley & William Kerr, 2016. "Transition to Clean Technology," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 52-104.
    7. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, pages 82-94.
    8. Michel Berthélemy & Petyo Bonev & Damien Dussaux & Magnus Söderberg, 2017. "Methods for strengthening a weak instrument in the case of a persistent treatment," GRI Working Papers 265, Grantham Research Institute on Climate Change and the Environment.
    9. Liu, Chu-An & Tao, Jing, 2016. "Model selection and model averaging in nonparametric instrumental variables models," MPRA Paper 69492, University Library of Munich, Germany.
    10. Wang, Wenjie & Kaffo, Maximilien, 2016. "Bootstrap inference for instrumental variable models with many weak instruments," Journal of Econometrics, Elsevier, pages 231-268.
    11. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, pages 40-58.
    12. Enrique Moral-Benito, 2011. "Model averaging in economics," Working Papers 1123, Banco de España;Working Papers Homepage.
    13. Kuersteiner, Guido M., 2012. "Kernel-weighted GMM estimators for linear time series models," Journal of Econometrics, Elsevier, pages 399-421.
    14. Enrique Moral-Benito, 2010. "Model Averaging in Economics," Working Papers wp2010_1008, CEMFI.
    15. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, pages 176-195.
    16. Martins, Luis F. & Gabriel, Vasco J., 2014. "Linear instrumental variables model averaging estimation," Computational Statistics & Data Analysis, Elsevier, pages 709-724.
    17. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    18. Zhang, Xinyu & Ullah, Aman & Zhao, Shangwei, 2016. "On the dominance of Mallows model averaging estimator over ordinary least squares estimator," Economics Letters, Elsevier, pages 69-73.
    19. Xiaohong Chen & David Jacho-Chávez & Oliver Linton, 2012. "Averaging of moment condition estimators," CeMMAP working papers CWP26/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM," PIER Working Paper Archive 14-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 04 Aug 2014.
    21. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, pages 40-58.

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