Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Standard estimators of risk premia in linear asset pricing models are biased if some priced factors are omitted. We propose a three-pass method to estimate the risk premium of an observable factor, which is valid even when not all factors in the model are specified or observed. The risk premium of the observable factor can be identified regardless of the rotation of the other control factors if together they span the true factor space. Our approach uses principal components of test asset returns to recover the factor space and additional regressions to obtain the risk premium of the observed factor.