2016European Accounting ReviewRequires access

Addressing Unobserved Selection Bias in Accounting Studies: The Bias Minimization Method

Michael J. Peel

Open publisher page 36 citations

Abstract

This note explains the minimum-biased estimator (MBE), which accounting researchers can use to analyze the robustness of regression or propensity score-matched treatment estimates to unobserved selection (endogeneity) bias. Based on the principles of the Heckman treatment model, the MBE entails estimating matched treatment effects within a range of propensity scores that minimizes unobserved selection bias. A major advantage of the MBE is that an instrumental variable is not required. The potential utility of the MBE in accounting studies is highlighted, and a familiar empirical illustration is provided.

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What this paper is about

This note explains the minimum-biased estimator (MBE), which accounting researchers can use to analyze the robustness of regression or propensity score-matched treatment estimates to unobserved selection (endogeneity) bias. Based on the principles of the Heckman treatment model, the MBE entails estimating matched treatment effects within a range of propensity scores that minimizes unobserved selection bias. A major advantage of the MBE is that an instrumental variable is not required. The potential utility of the MBE in accounting studies is highlighted, and a familiar empirical illustration is provided.

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Available abstract

This note explains the minimum-biased estimator (MBE), which accounting researchers can use to analyze the robustness of regression or propensity score-matched treatment estimates to unobserved selection (endogeneity) bias. Based on the principles of the Heckman treatment model, the MBE entails estimating matched treatment effects within a range of propensity scores that minimizes unobserved selection bias. A major advantage of the MBE is that an instrumental variable is not required. The potential utility of the MBE in accounting studies is highlighted, and a familiar empirical illustration is provided.

Key concepts: Endogeneity, Instrumental variable, Selection bias, Propensity score matching, Econometrics, Estimator, Omitted-variable bias, Robustness (evolution)

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