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The LIML estimator has finite moments!

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  • Anderson, T.W.

Abstract

The Limited Information Maximum Likelihood estimator of the vector of coefficients of a structural equation in a simultaneous equation model is the vector that defines the linear combination maximizing the effect variance relative to the error variance. If this "eigenvector" solution is normalized by setting a designated coefficient equal to 1, the second-order moment of the estimator may be unbounded. However, the second-order moment is finite if the normalization sets the sample error variance of the linear combination equal to 1.

Suggested Citation

  • Anderson, T.W., 2010. "The LIML estimator has finite moments!," Journal of Econometrics, Elsevier, vol. 157(2), pages 359-361, August.
    • Handle: RePEc:eee:econom:v:157:y:2010:i:2:p:359-361
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    References listed on IDEAS

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    1. Fuller, Wayne A, 1977. "Some Properties of a Modification of the Limited Information Estimator," Econometrica, Econometric Society, vol. 45(4), pages 939-953, May.
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    Cited by:

    1. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
    2. Eric Blankmeyer, 2020. "NISE Estimation of an Economic Model of Crime," Papers 2003.07860, arXiv.org.
    3. Gareth Liu-Evans & Garry DA Phillips, 2023. "The Bias of the Modified Limited Information Maximum Likelihood Estimator (MLIML) in Static Simultaneous Equation Models Abstract: A higher-order approximation is made to the bias of the modified LIML," Working Papers 202303, University of Liverpool, Department of Economics.
    4. Jerome R. Simons, 2023. "Inference on eigenvectors of non-symmetric matrices," Papers 2303.18233, arXiv.org, revised Apr 2023.
    5. Blankmeyer, Eric, 2021. "Explorations in NISE Estimation," MPRA Paper 108179, University Library of Munich, Germany.
    6. 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.

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