VMACpp

Data matrix of a k-dimensional VMA process with each column
containing one time series

A logical switch to include the mean vector.
The default is to include the mean vector in estimation.

include.mean

A logical matrix used to fix parameter to zero

fixed

Parameter estimates for use in model simplification

beta

Standard errors of parameter estimates for use in model
simplification

sebeta

A logical switch to select parameters to be included in estimation

prelim

A logical switch to control the amount of output

details

Threshold for t-ratio used to fix parameter to zero. Default is 2.

thres

Performs VMA estimation using the conditional multivariate
Gaussian likelihood function. This is the same function as VMA, with the likelihood function implemented in C++ for efficiency.

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.

VMACpp: Vector Moving Average Model (Cpp)

Description

Usage

Arguments

Value

References

See Also

Examples