A robust instrumental-variables estimator
AbstractThe classical instrumental-variables estimator is extremely sensitive to the presence of outliers in the sample. This is a concern because outliers can strongly distort the estimated effect of a given regressor on the dependent variable. Although outlier diagnostics exist, they frequently fail to detect atypical observations because they are themselves based on nonrobust (to outliers) estimators. Furthermore, they do not take into account the combined influence of outliers in the first and second stages of the instrumental-variables estimator. In this article, we present a robust instrumental-variables estimator, initially proposed by Cohen Freue, Ortiz-Molina, and Zamar (2011, Working paper: http://www.stat.ubc.ca/˜ruben/website/cv/cohen-zamar.pdf ), that we have programmed in Stata and made available via the robivreg command. We have improved on their estimator in two different ways. First, we use a weighting scheme that makes our estimator more efficient and allows the computations of the usual identification and overidentifying restrictions tests. Second, we implement a generalized Hausman test for the presence of outliers. Copyright 2012 by StataCorp LP.
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Bibliographic InfoArticle provided by StataCorp LP in its journal Stata Journal.
Volume (Year): 12 (2012)
Issue (Month): 2 (June)
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- Darwin Ugarte Ontiveros & Vincenzo Verardi, 2012. "Supposedly Strong Instruments and Good Leverage Points," Working Papers 1203, University of Namur, Department of Economics.
- Lorenzo Camponovo & Taisuke Otsu, 2014.
"Robustness of bootstrap in instrumental variable regression,"
STICERD - Econometrics Paper Series
/2014/572, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Lorenzo Camponovo & Taisuke Otsu, 2011. "Robustness of Bootstrap in Instrumental Variable Regression," Cowles Foundation Discussion Papers 1796, Cowles Foundation for Research in Economics, Yale University.
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