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We propose regression-based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. We allow for state variables that are cross-sectional pricing factors, forecasting variables for the price of risk, or factors that are both. The estimators extend static cross-sectional asset pricing estimators to dynamic pricing kernels. We provide multistage standard errors necessary to conduct inference for asset pricing tests. An application to the joint pricing of stocks and bonds features cross-sectional pricing properties with small average pricing errors as well as strongly time-varying, highly significant prices of risk. The application shows that there is a role for all three types of factors.