This is an accomplishment based renewal project. The goal of this project is to bring some fresh thoughts on the evaluation of empirical models or to economic theories; although two established building blocks will be used. The first comes from panel analysis and discusses the possibility of `spurious stochastics` occurring in a dynamic panel model specification, using data from regions that differ greatly in size, but where the size effect is not properly modeled. Size itself may be slowly evolving and so not be captured by the use of fixed effects. Initial simulations suggest that this is potentially an important problem. One that follows from it is how to evaluate panel models, and thus how to compare models. The second block is consideration of a general theory of forecasting, with general cost t functions and an emphasis on predictive distribution functions. New tests for forecast errors arise and a better appreciation of the appropriate modeling strategy to use depending on ones objective and knowledge. The majority of the currently available methods of evaluation, such as measures of goodness of fit, correctness of specification and encompassing, are statistical measures. It will be argued that a better approach considers the value to the economy of a new model or theory, or at least the aid it gives to decision makers. This is the approach is already taken in finance and it could be more widely used in forecasting. The implications of this viewpoint will be explored and analyzed
