Instrumental variable estimation in the presence of many moment conditions
AbstractThis paper develops shrinkage methods for addressing the “many instruments” problem in the context of instrumental variable estimation. It has been observed that instrumental variable estimators may behave poorly if the number of instruments is large. This problem can be addressed by shrinking the influence of a subset of instrumental variables. The procedure can be understood as a two-step process of shrinking some of the OLS coefficient estimates from the regression of the endogenous variables on the instruments, then using the predicted values of the endogenous variables (based on the shrunk coefficient estimates) as the instruments. The shrinkage parameter is chosen to minimize the asymptotic mean square error. The optimal shrinkage parameter has a closed form, which makes it easy to implement. A Monte Carlo study shows that the shrinkage method works well and performs better in many situations than do existing instrument selection procedures.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 165 (2011)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/jeconom
TSLS; LIML; Shrinkage estimator; Instrumental variables;
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- Kazuhiko Hayakawa, 2008. "On the Effect of Nonstationary Initial Conditions in Dynamic Panel Data Models," Hi-Stat Discussion Paper Series d07-245, Institute of Economic Research, Hitotsubashi University.
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