A primary focus of empirical earnings research is whether accounting earnings contain meaningful and timely information that can be used by market participants to value securities. Empirically, accounting earnings numbers are considered to be reliable representations of economic performance if abnormal returns and unexpected earnings are associated. A critical assumption made when estimating this association by ordinary least squares (OLS) is that the explanatory variable (unexpected earnings) is independent of the error term. If unexpected earnings is measured with error, this assumption is violated, and OLS will produce biased estimates of the parameters. It\u27s widely recognized that empirical measures of unexpected earnings contain measurement error, biasing the OLS estimate of the earnings response coefficient (ERC) towards zero. Prior studies have used various techniques to address measurement error in the unexpected earnings proxy. These techniques include: using multiple proxies for unexpected earnings; including lagged security returns; grouping by size of abnormal returns; using reverse regression; and using instrumental variables. No general conclusion has been reached, however, about which technique is the \u27best\u27 or whether any of these techniques completely eliminates bias in the ERC. Therefore, in this study I provide a discussion of the conditions under which each error-reduction technique is most effective in reducing measurement error bias in the ERC. In addition, I provide empirical evidence on each technique\u27s ability to reduce coefficient bias. The empirical results show that none of these techniques is useful at reducing measurement error bias in the ERC. The ERC estimate increases by at most 8%. I also introduce and assess a new technique that yields consistent parameter estimates in the presence of measurement error (Fuller (1987, 1991)). The empirical results indicate that this technique is fairly successful at reducing measurement error bias in the ERC. The ERC estimate increases by as much as 52%. This technique is most successful at eliminating bias in the ERC estimates of larger firms and for firms whose earnings changes are primarily transitory. This technique provides a promising alternative approach to address measurement error bias in the ERC when investigating the information content of earnings
