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...

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...

In this paper, we investigate the causal effects of public and private debts on U.S. output dynamics. We estimate a battery of Cointegrated Structural Vector Autoregressive models, and we identify structural shocks by employing Independent Component Analysis, a data-driven technique which avoids...

The size of the economy-wide rebound effect is crucial for estimating the contribution that energy efficiency improvements can make to reducing energy use and greenhouse gas emissions. We provide the first empirical general equilibrium estimate of the economy-wide rebound effect. We use a...

Improving energy efficiency is often considered to be one of the keys to reducing greenhouse gas emissions. However, efficiency gains also reduce the cost of energy services and may even reduce the price of energy, resulting in energy use rebounding and potential energy use savings being eaten...

We propose a statistical identification procedure for structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise models to...