Vector autoregressions and reduced form representations of DSGE models

Graphical Models for Structural Vector Autoregressions

The identification of a VAR requires differentiating between correlation and causation. This paper presents a method to deal with this problem. Graphical models, which provide a rigorous language to analyze the statistical and logical properties of causal relations, associate a particular set of vanishing partial correlations to every possible causal structure. The structural form is described ...

Vector Autoregressions and Cointegration*

compactifications and representations of transformation semigroups

this thesis deals essentially (but not from all aspects) with the extension of the notion of semigroup compactification and the construction of a general theory of semitopological nonaffine (affine) transformation semigroup compactifications. it determines those compactification which are universal with respect to some algebric or topological properties. as an application of the theory, it is i...

Bayesian Vector Autoregressions

This article provides an introduction to the burgeoning academic literature on Bayesian Vector Autoregressions, benchmark models for applied macroeconomic research. We first explain Bayes’ theorem and the derivation of the closed-form solution for the posterior distribution of the parameters of the model given data. We further consider parameter shrinkage, a distinguishing feature of the prior ...

Dynamic Stochastic General Equilibrium (DSGE) Priors for Bayesian Vector Autoregressive (BVAR) Models: DSGE Model Comparison

This Paper describes a procedure for constructing theory restricted prior distributions for BVAR models. The Bayes Factor, which is obtained without any additional computational effort, can be used to assess the plausibility of the restrictions imposed on the VAR parameter vector by competing DSGE models. In other words, it is possible to rank the amount of abstraction implied by each DSGE mode...