This paper studies asset returns adopting an alternative strategy to assess a model's goodness of fit. Based on spectral analysis, this approach considers a model as an approximation to the process generating the observed data, and characterizes the dimensions for which the model provides a good approximation and those for which it does not. Our aim is to offer new evidence regarding the size and the location of approximation errors of a set of stochastic growth models considered to be decisive steps in the progress of the asset pricing research program. Our specific objective is to reevaluate the results of Jermann's (1998) model extending the calculations to the spectral domain. Spectral results are relatively satisfactory: the benchmark model needs very few contributions of approximation errors to account for the empirical equity premium. Second, the location of the approximation errors, when they are substantial, seems to be essentially concentrated at high frequencies.
