Add to ORKGRePEc Working Papers Series: No: 19/2008In this paper graphical modelling is used to select a sparse structure for a multivariate time series model of New Zealand interest rates. In particular, we consider a recursive structural vector autoregressions that can subsequently be described parsimoniously by a directed acyclic graph, which could be given a causal interpretation. A comparison between competing models is then made by considering likelihood and economic theory
: A general class of structural vector autoregressions (VAR) is considered containing most variants ...
In this thesis we apply graphical statistics models for analyzing causality relations among various ...
We introduce graphical time series models for the analysis of dynamic relationships among variables ...
In this paper graphical modelling is used to select a sparse structure for a multivariate time serie...
In canonical vector time series autoregressions, which permit dependence only on past values, the er...
Structural Vector Autoregressions allow dependence among contemporaneous vari-ables. If such models ...
Structural vector autoregressions allow contemporaneous series dependence and assume errors with no ...
This paper proposes a Bayesian, graph-based approach to identification in vector autoregressive (VAR...
Technology has impacted extensively on the opera-tions of financial markets which are inhabited by a...
This paper proposes a Bayesian, graph-based approach to identification in vector autoregressive (VAR...
Technology has impacted extensively on the operations of financial markets which are inhabited by a ...
An objective Bayes approach based on graphical modeling is proposed to learn the contemporaneous dep...
Title: Study of the dependence structure in economic and financial data Author: Radana Hlavandová De...
In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predic...
: A general class of structural vector autoregressions (VAR) is considered containing most variants ...
In this thesis we apply graphical statistics models for analyzing causality relations among various ...
We introduce graphical time series models for the analysis of dynamic relationships among variables ...
In this paper graphical modelling is used to select a sparse structure for a multivariate time serie...
In canonical vector time series autoregressions, which permit dependence only on past values, the er...
Structural Vector Autoregressions allow dependence among contemporaneous vari-ables. If such models ...
Structural vector autoregressions allow contemporaneous series dependence and assume errors with no ...
This paper proposes a Bayesian, graph-based approach to identification in vector autoregressive (VAR...
Technology has impacted extensively on the opera-tions of financial markets which are inhabited by a...
This paper proposes a Bayesian, graph-based approach to identification in vector autoregressive (VAR...
Technology has impacted extensively on the operations of financial markets which are inhabited by a ...
An objective Bayes approach based on graphical modeling is proposed to learn the contemporaneous dep...
Title: Study of the dependence structure in economic and financial data Author: Radana Hlavandová De...
In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predic...
: A general class of structural vector autoregressions (VAR) is considered containing most variants ...
In this thesis we apply graphical statistics models for analyzing causality relations among various ...
We introduce graphical time series models for the analysis of dynamic relationships among variables ...