Estimating and Testing Beta Pricing Models: Alternative Methods and Their Performance in Simulations

In this paper, we conduct a simulation analysis of the Fama and MacBeth (1973) two-pass procedure, as well as maximum likelihood (ML) and generalized method of moments estimators of cross-sectional expected return models. We also provide some new analytical results on computational issues, the relations between estimators, and asymptotic distributions under model misspecification. The GLS estimator is often much more precise than the usual OLS estimator, but it displays more bias as well. A "truncated" form of ML performs quite well overall in terms of bias and precision, but produces less reliable inferences than the OLS estimator.

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