This paper proposes a tripartite framework of design, evaluation, and post-evaluation analysis for generating and interpreting economic forecasts. This framework's value is illustrated by re-examining mean square forecast errors from dynamic models and nonlinearity biases from empirical forecasts of U.S. external trade. Previous studies have examined properties such as nonlinearity bias and the possible nonmonotonicity and nonexistence of mean square forecast errors in isolation from other aspects of the forecasting process, resulting in inefficient forecasting techniques and seemingly puzzling phenomena. The framework developed reveals how each such property follows from systematically integrating all aspects of the forecasting process.