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...

Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this paper, we present a recently developed method for exploiting non-Gaussianity in the data for estimating such models, with the aim of capturing the causal structure...

This study investigates the effects of a monetary policy shock on real output and prices, by means of a novel distribution-free nonrecursive identification scheme for structural vector autoregressions. Structural shocks are assumed to be mutually independent. The identification procedure is...

This paper proposes a new method for empirically validate simulation models that generate artificial time series data comparable with real-world data. The approach is based on comparing structures of vector autoregression models which are estimated from both artificial and real-world data by...

In this paper we present a semi-automated search procedure to deal with the problem of the identification of the causal structure related to a vector autoregressive model. The structural form of the model is described by a directed graph and from the analysis of the partial correlations of the...

Independent Component Analysis (ICA) is a statistical method that transforms a set of random variables in least dependent linear combinations. Under the assumption that the observed data are mixtures of non-Gaussian and independent processes, ICA is able to recover the underlying components, but...