A literature review of new methods in empirical asset pricing: omitted-variable and errors-in-variable bias

Standard procedures in empirical asset pricing suffer from various issues that are common to all regression-based methods. This work reviews recently introduced approaches that aim to mitigate problems associated with omitted factors and errors-in-variables. New methods addressing the omitted-variable bias suggest procedures for selecting appropriate control variables, aggregating the information from a large set of factors, or making existing methods robust against omitted factors. While the omitted-variable problem is present in almost all standard empirical asset pricing methods, the errors-in-variables problem is largely limited to the estimation of factor premia via two-pass regressions. New methods addressing the errors-in-variable bias implement an instrumental variable approach, suggest a generalized version of the widely used portfolio sorts procedure, or correct estimates based on an analytic expression for the bias. Ultimately, all of these new methods represent highly relevant advances for the area of empirical asset pricing, and the possibility to synthesize the most promising approaches might be worthwhile to investigate in the future.