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Efficient, regression-based estimation of dynamic asset pricing models

We study regression-based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. These estimators extend static cross-sectional asset pricing estimators to settings where prices of risk vary with observed state variables. We identify conditions under which four-stage regression-based estimators are efficient and also present alternative, closed-form linearized maximum likelihood (LML) estimators. We provide multi-stage standard errors necessary to conduct inference for asset pricing tests. In empirical applications, we find that time-varying prices of risk are pervasive, thus favoring dynamic cross-sectional asset pricing models over standard unconditional specifications.