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When Can We Determine the Direction of Omitted Variable Bias of OLS Estimators?

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  • Deepankar Basu

    (Department of Economics, University of Massachusetts - Amherst)

Abstract

Omitted variable bias (OVB) of OLS estimators is a serious and ubiquitous problem in social science research. Often researchers use the direction of the bias in substantive arguments or to motivate estimation methods to deal with the bias. This paper offers a geometric interpretation of OVB that highlights the difficulty in ascertaining its sign in any realistic setting and cautions against the use of direction-of-bias arguments. This analysis has implications for comparison of OLS and IV estimators too.

Suggested Citation

  • Deepankar Basu, 2018. "When Can We Determine the Direction of Omitted Variable Bias of OLS Estimators?," UMASS Amherst Economics Working Papers 2018-16, University of Massachusetts Amherst, Department of Economics.
    • Handle: RePEc:ums:papers:2018-16
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    File URL: http://www.umass.edu/economics/publications/2018-16.pdf
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    References listed on IDEAS

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    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    2. David H. Autor & David Dorn & Gordon H. Hanson, 2013. "The China Syndrome: Local Labor Market Effects of Import Competition in the United States," American Economic Review, American Economic Association, vol. 103(6), pages 2121-2168, October.
    3. Abhijit Banerjee & Lakshmi Iyer, 2005. "History, Institutions, and Economic Performance: The Legacy of Colonial Land Tenure Systems in India," American Economic Review, American Economic Association, vol. 95(4), pages 1190-1213, September.
    4. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    5. repec:fth:prinin:455 is not listed on IDEAS
    6. Blackburn, McKinley L & Neumark, David, 1995. "Are OLS Estimates of the Return to Schooling Biased Downward? Another Look," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 217-230, May.
    7. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    8. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    9. Kristin J. Forbes, 2000. "A Reassessment of the Relationship between Inequality and Growth," American Economic Review, American Economic Association, vol. 90(4), pages 869-887, September.
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    Cited by:

    1. Deepankar Basu, 2018. "Bias of OLS Estimators due to Exclusion of Relevant Variables and Inclusion of Irrelevant Variables," UMASS Amherst Economics Working Papers 2018-19, University of Massachusetts Amherst, Department of Economics.

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    More about this item

    Keywords

    omitted variable bias; ordinary least squares;

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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