Estimation of structural vector autoregressive models

Causal Search in Structural Vector Autoregressive Models

This paper reviews a class of methods to perform causal inference in the framework of a structural vector autoregressive model. We consider three different settings. In the first setting the underlying system is linear with normal disturbances and the structural model is identified by exploiting the information incorporated in the partial correlations of the estimated residuals. Zero partial co...

Vector Autoregressive Models

Estimating Structured Vector Autoregressive Models

While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent. We consider estimating structured VAR (vector auto-regressive model), where the structure can be captured by any suitable norm, e.g., Lasso, group Lasso, order weighted Lasso, etc. I...

Structural Changes in the Cointegrated Vector Autoregressive

This paper generalizes the cointegrated vector autoregressive model of Johansen (1988) to allow for structural changes. Estimation under various hypotheses is made possible by a new estimation technique, that makes it simple to derive a number of interesting likelihood ratio tests. E.g., the test for m structural changes against m+ k structural changes (occurring at fixed points in time), m ∈ N...

Factor vector autoregressive estimation: a new approach

In this paper a new approach to factor vector autoregressive estimation, based on Stock and Watson (Implications of dynamic factor models for VAR analysis, NBER Working Paper, no. 11467, 2005), is introduced. In addition to sharing all the relevant features of the Stock–Watson approach, in its static formulation, the proposed method has the advantage of allowing for a more clear-cut interpretat...