BVAR

A matrix of vector time series, each column represents a series.

The precision matrix of the coefficient matrix. With constant, 
the dimension of C is (kp+1)-by-(kp+1). The covariance matrix of the 
prior for the parameter vec(Beta) is Kronecker(Sigma_a,C-inverse).

A k-by-k covariance matrix to be used as prior for the Sigma_a matrix

The degrees of freedom used for prior of the Sigma_a matrix, the covariance matrix of the innovations. Default is n0=5.

The prior mean for the parameters. Default is set to NULL, implying that the 
prior means are zero.

Phi0

A logical switch controls the constant term in the VAR model. Default is to include the constant term.

include.mean

Estimate a VAR(p) model using Bayesian approach, including the 
use of Minnesota prior

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

BVAR: Bayesian Vector Autoregression

Description

Usage

Arguments

Value

Details

References

Examples