We propose the unified approach to construct the non–informative prior for time–series econometric models that are invariant under some group of transformations. We show that this invariance property characterizes some of the most popular models hence the applicability of the proposed...

The aim of the paper is to study the nature of normalization in Structural VAR models. Noting that normalization is the integral part of identification of a model, we provide a general characterization of the normalization. In consequence some the easy–to–check conditions for a Structural...

The article presents the problem of identification in parametric models from an 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...

The paper aims at systematic placement of identification concept within Bayesian approach. Pointing to some deficiencies of the standard Bayesian language to describe identification problem we propose several useful characterizations that seem to be intuitively sound and attractive given their...

We provide some generalization and clarification of the identification conditions for Structural VAR (SVAR) models given in Rubio–Ramírez et al (2010). In particular we show that their basic sufficient condition is also necessary. In addition we give necessary and sufficient conditions for...

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...

The paper provides a novel Bayesian methodological framework to estimate structural VAR (SVAR) models with recursive identification schemes that allows for the inclusion of over-identifying restrictions. The proposed framework enables the researcher to (i) elicit the prior on the non-zero...

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...

The paper provides a novel Bayesian methodological framework to estimate structural VAR (SVAR) models with recursive identification schemes that allows for the inclusion of over-identifying restrictions. The proposed framework enables the researcher to (i) elicit the prior on the non-zero...