All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models
Asymptotic Principal Component Analysis
ARCH test for univariate time series
Backtesting of a scalar ARIMA model
BEKK Model
Back-Test of a Transfer Function Model with Two Input Variables
Bayesian Vector Autoregression
Cross-Correlation Matrices
Common Volatility
Compute the Corner table for transfer function model specification
Dynamic Cross-Correlation Model Fitting
Preliminary Fitting of DCC Models
Difference of multivariate time series
Extended Cross-Correlation Matrices
Error-Correction VAR Models
Error-Correction VAR Model 1
Exponentially Weighted Moving-Average Volatility
Forecast Error Variance Decomposition
Granger Causality Test
Constrained Factor Model
Monthly simple returns of the stocks of International Business Machine...
Fitting a VARMA Model via Kronecker Index
Kronecker Index Identification
Prediction of a fitted VARMA model via Kronfit, using Kronecker indice...
Kronecler Index Specification
Multivariate ARCH test
Multivariate Conditional Heteroscedastic Model Checking
Multivariate Cholesky Volatility Model
Multivariate Linear Model
Multivariate Ljung-Box Q Statistics
Square Root Matrix
Multivariate t-Copula Volatility Model
MTS Internal Functions
Multivariate Time Series
Multivariate Time Series Diagnostic Checking
Multivariate Time Series Plot
Polynomial Matrix Product
Alternative Polynomial Matrix Product
Pi Weight Matrices
Psi Wights Matrices
Quarterly real gross domestic products of United Kingdom, Canada, and ...
Refining Error-Correction Model for VAR series
Refining ECM for a VAR process
Refining VARMA Estimation via Kronecker Index Approach
Refining a Regression Model with Time Series Errors
Refining Estimation of VARMA Model via SCM Approach
Refining a Seasonal VARMA Model
Refining a VAR Model
Refining VARMA Estimation
Refining a VARX Model
Refining VMA Models
Refining VMA Estimation via the Exact Likelihood Method
Regression Model with Time Series Errors
Prediction of a fitted regression model with time series errors
Recursive Least Squares
Sample Constrained Correlations
Scalar Component Model Fitting
Scalar Component Identification
Scalar Component Model Specification II
Scalar Component Model specification
Seasonal VARMA Model Estimation
Seasonal VARMA Model Estimation (Cpp)
Prediction of a fitted multiplicative seasonal VARMA model
Stock-Watson Diffusion Index Forecasts
Monthly simple returns of ten U.S. stocks
Transfer Function Model
Transfer Function Model with One Input
Transfer Function Model with Two Input Variables
Vector Autoregressive Model
Vector Autoregressive Moving-Average Models
Autocovariance Matrices of a VARMA Model
Vector Autoregressive Moving-Average Models (Cpp)
Impulse Response Functions of a VARMA Model
VARMA Prediction
Generating a VARMA Process
VAR Order Specification
VAR order specification I
VAR Prediction
VAR Psi-weights
VAR Model with Selected Lags
VAR Model with Exogenous Variables
Impluse response function of a fitted VARX model
VARX Order Specification
VARX Model Prediction
Half-Stacking Vector of a Symmetric Matrix
Matrix constructed from output of the Vech Command. In other words, re...
Vector Moving Average Model
Vector Moving Average Model (Cpp)
VMA Estimation with Exact likelihood
VMA Order Specification
VMA Model with Selected Lags
VARMA Model with Missing Value
Partial Missing Value of a VARMA Series
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.