The paper presents the problem of identification in parametric models from the algebraic point of view. We argue that it is not just another perspective but the proper one. That is using our approach we can see the very nature of the identification problem, which is slightly different than that...

This paper introduces a time domain framework to analyze global identification of stochastically nonsingular DSGE models. A formal identification condition is established that relies on the restrictions linking the observationally equivalent minimal state space representations and on the...

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. In the...