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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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    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.
    2. Chiaverina, Pierre & Drogué, Sophie & Jacquet, Florence, 2024. "Do Farmers Participating in Short Food Supply Chains Use Less Pesticides? Evidence from France," Ecological Economics, Elsevier, vol. 216(C).

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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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