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Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note

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  • Fran篩s-Éric Racicot

Abstract

Econometricians have long recognized the need to account in some way for measurement errors, specification errors and endogeneity to ensure that the ordinary least squares estimator is consistent. This article introduces a new generalized method of moments estimator that relies on robust instruments to estimate panel data regression models containing errors in variables. We show how this GMM approach can be generalized for the panel data framework using higher moments and cumulants as instruments. The new instruments, engineered for greater robustness, are proposed to tackle the pervasive problem of weak instruments.

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  • Fran篩s-Éric Racicot, 2015. "Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note," Applied Economics, Taylor & Francis Journals, vol. 47(10), pages 981-989, February.
    • Handle: RePEc:taf:applec:v:47:y:2015:i:10:p:981-989
    DOI: 10.1080/00036846.2014.985373
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    References listed on IDEAS

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    1. Francois-Éric Racicot, 2007. "Techniques alternatives d’estimation et tests en présence d’erreurs de mesure sur les variables explicatives," RePAd Working Paper Series UQO-DSA-wp022007, Département des sciences administratives, UQO.
    2. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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    Cited by:

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    8. Racicot, François-Éric & Rentz, William F., 2018. "Does Illiquidity Matter? An Errors-in-Variables Perspective/¿Es importante la iliquidez? Un análisis desde el enfoque de errores en variables," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 251-262, Enero.
    9. López-García, M.N. & Trinidad-Segovia, J.E. & Sánchez-Granero, M.A. & Pouchkarev, I., 2021. "Extending the Fama and French model with a long term memory factor," European Journal of Operational Research, Elsevier, vol. 291(2), pages 421-426.
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