| Name : R-MTS
| |
| Version : 1.2.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp156.2.1
| Date : 2024-09-10 06:05:49
|
| Group : Development/Libraries/Other
| Source RPM : R-MTS-1.2.1-lp156.2.1.src.rpm
|
| Size : 1.02 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models
|
Description :
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.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.6/x86_64 |
