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Principal Components Instrumental Variable Estimation

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  • Winkelried, D.
  • Smith, R.J.

Abstract

Instrumental variable estimators can be severely biased in finite samples when the degree of overidentification is high or when the instruments are weakly correlated with the endogenous regressors. This paper proposes an estimator based on the use of the principal components of the instruments as a means of dealing with these issues. By promoting parsimony, the proposed estimator can exhibit considerably lower bias, often without giving up asymptotic efficiency. To make the estimator operational, a simple but flexible rule to select the relevant components for estimation is suggested. Simulation evidence shows that this approach yields significant finite sample improvements over other instrumental variable estimators.

Suggested Citation

  • Winkelried, D. & Smith, R.J., 2011. "Principal Components Instrumental Variable Estimation," Cambridge Working Papers in Economics 1119, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1119
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    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1119.pdf
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    References listed on IDEAS

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    1. Hasselt, Martijn van, 2010. "Many Instruments Asymptotic Approximations Under Nonnormal Error Distributions," Econometric Theory, Cambridge University Press, vol. 26(02), pages 633-645, April.
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    Cited by:

    1. David M. Vetter & Kaizô I. Beltrão & Rosa M. R. Massena, 2013. "The Impact of the Sense of Security from Crime on Residential Property Values in Brazilian Metropolitan Areas," Research Department Publications IDB-WP-415, Inter-American Development Bank, Research Department.
    2. Shanker Satyanath & Nico Voigtlaender & Hans-Joachim Voth, 2013. "Bowling for Fascism: Social Capital and the Rise of the Nazi Party," NBER Working Papers 19201, National Bureau of Economic Research, Inc.

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    Keywords

    Many instrument asymptotics; principal components;

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