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Can We Make Smart Choices Between Ols And Contaminated Iv Methods?

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  • Anirban Basu
  • Kwun Chuen Gary Chan

Abstract

In the outcomes research and comparative effectiveness research literature, there are strong cautionary tales on the use of instrumental variables (IVs) that may influence the newly initiated to shun this premier tool for casual inference without properly weighing their advantages. It has been recommended that IV methods should be avoided if the instrument is not econometrically perfect. The fact that IVs can produce better results than naïve regression, even in nonideal circumstances, remains underappreciated. In this paper, we propose a diagnostic criterion and related software that can be used by an applied researcher to determine the plausible superiority of IV over an ordinary least squares (OLS) estimator, which does not address the endogeneity of a covariate in question. Given a reasonable lower bound for the bias arising out of an OLS estimator, the researcher can use our proposed diagnostic tool to confirm whether the IV at hand can produce a better estimate (i.e., with lower mean square error) of the true effect parameter than the OLS, without knowing the true level of contamination in the IV. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Anirban Basu & Kwun Chuen Gary Chan, 2014. "Can We Make Smart Choices Between Ols And Contaminated Iv Methods?," Health Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 462-472, April.
    • Handle: RePEc:wly:hlthec:v:23:y:2014:i:4:p:462-472
    DOI: 10.1002/hec.2926
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    Cited by:

    1. Souvik Banerjee & Anirban Basu, 2021. "Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables," Econometrics, MDPI, vol. 9(1), pages 1-25, March.
    2. Rapp, Thomas & Chauvin, Pauline & Sirven, Nicolas, 2015. "Are public subsidies effective to reduce emergency care? Evidence from the PLASA study," Social Science & Medicine, Elsevier, vol. 138(C), pages 31-37.

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