To achieve the latter, we propose a Bayesian inference approach to analyze the dynamic interactions among macroeconomics variables in a graphical vector autoregressive model. The method decomposes the structural model into multivariate autoregressive and contemporaneous networks that can be represented in the...
To achieve the latter, we propose a Bayesian inference approach to analyze the dynamic interactions among macroeconomics variables in a graphical vector autoregressive model. The method decomposes the structural model into multivariate autoregressive and contemporaneous networks that can be represented in the...
tests .Rbuildignore .gitignore DESCRIPTION NAMESPACE NEWS README.md mgm.Rproj Repository files navigation README mgm The package includes functions to estimate, visualize and resample time-varying k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models. ...
This paper proposes a Bayesian, graph‐based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of the structural VAR model are represented by two different graphs. We also provide an effic...
We present the R-package mgm for the estimation of k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data sets consisting of mixed types of variables...
Currently, the package offers functions to fit a graphical autoregressive -- GAR(1) model through a 3-step procedure. Dependencies Please make sure that the following packages are installed before using the R package SGM. install.packages(c("doParallel", "foreach", "gmp")) Installation The ...
This model leads to a multi-scale representation of the spatio-temporal process. We propose a statistical procedure to estimate the multi-scale structure and the model parameters in the case of the vector autoregressive model with drift. Modeling and estimation tasks are illustrated on simulated and...
Synthetic data from cases with failed pattern(s) and anomalous node are simulated to validate the proposed approaches, then compared with the performance of vector autoregressive (VAR) model-based root-cause analysis. The results show that: (1) $S^3$ and $A^3$ approaches can obtain high ...
Vector autoregressive models The vector autoregressive (VAR) model is a straightforward extension of the univariate autoregressive model [25] and describes how the values of the variables at time t depend linearly on the values at previous time points. For the sake of simplicity, we restrict oursel...
Large Vector AutoregressionModel SelectionPrior DistributionSparse Graphical ModelsIn high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predictors relative to the number of observations, and a lack of efficiency in estimation and forecasting. In this context, ...