Identification-robust and simultaneous inference in multifactor asset pricing models

Abstract

This paper proposes exact identification-robust confidence sets for the zero-beta rate and ex-post factor prices in asset pricing models. Exploiting the information from the cross-sectional intercept allows us to impose or formally test model-consistent restrictions, including those resulting from traded factors in excess of the zero beta-rate or from return spreads. Analytical projection-based solutions for confidence set outcomes are developed. The proposed procedures are extended to the case of missing factors. Empirical and simulation results with traded and non-traded factors show that model-consistent restrictions and elusive factors can materially affect model fit, identification, inference and temporal constancy of pricing influence.