JMulTi 4.24 univariate and multivariate analysis software

JMulTi is an interactive software designed for univariate and multivariate time series analysis. It has a Java graphical user interface that uses an external engine for statistical computations. Implemented features include VAR/VEC modelling but also methods that are not yet in widespread use.

JMulTi was originally designed as a tool for certain econometric procedures in time series analysis that are especially difficult to use and that are not available in other packages, like Impulse Response Analysis with bootstrapped confidence intervals for VAR/VEC modelling. Now many other features have been integrated as well to make it possible to convey a comprehensive analysis. Limitations of this software can be overcome by exporting datasets or computation results and use them with other programs.

Econometric Features
Initial Analysis

various tools for creating, transforming, editing time series
Unit Root tests: ADF, HEGY (quarterly, monthly), Schmidt-Phillips, KPSS, Unit Root test with structural break
Cointegration tests: Johansen Cointegration test with response surfaces, Saikkonen & Lütkepohl test
kernel density estimation
spectral density plots
crossplots
autocorrelation analysis

VAR (can be used for univariate modelling as well)

VAR modelling (with arbitrary deterministic/exogenous variables)
subset model estimation
output in matrix form
automatic model selection (various strategies based on information criteria)
residual analysis with tests for nonnormality, autocorrelation, ARCH, spectrum, kernel density, autocorrelation plots, crosscorrelation
GARCH analysis for residuals
Impulse Responses with bootstrapped confidence intervals also for accumulated responses, orthogonal and forecast error versions
Forecast Error Variance Decomposition
forecasting, also levels from 1st differences, asymptotic confidence intervals for levels
causality tests
stability analysis: bootstrapped Chow tests, recursive parameters, recursive residuals, CUSUM test
SVAR modelling: AB model, Blanchard-Qua Model with bootstrapped standard errors
SVAR Forecast Error Variance Decomposition
SVAR Impulse Responses with bootstrapped confidence intervals


VECM

VECM modelling (with arbitrary deterministic/exogenous variables)
restrictions on cointegration space, Wald test for beta restrictions
Johansen, Two Stage, S2S estimation procedures
EC term can be fully or partly predetermined
subset model estimation
output in matrix form
automatic model selection (various strategies based on information criteria)
residual analysis with tests for nonnormality, autocorrelation, ARCH, spectrum, kernel density, autocorrelation plots, crosscorrelation
Impulse Responses with bootstrapped confidence intervals also for accumulated responses, orthogonal and forecast error versions
Forecast Error Variance Decomposition
forecasting, also levels from 1st differences, asymptotic confidence intervals for levels
causality tests
stability analysis: bootstrapped Chow tests, recursive parameters, recursive eigenvalues
SVEC modelling with bootstrapped standard errors
SVEC Forecast Error Variance Decomposition
SVEC Impulse Responses with bootstrapped confidence intervals


GARCH Analysis

univariate ARCH, GARCH, T-GARCH estimation with different error distributions
residual analysis for ARCH residuals with robustified test for no remaining ARCH (S. Lundbergh, T. Teraesvirta), plotting of variance process, kernel density for residuals
multivariate GARCH(1,1) estimation, residual analysis, plotting of variance process together with univariate estimates, kernel density for residuals


Smooth Transition Regression

STR model specification with exogenous/deterministic variables
linearity tests
STR estimation
various specification tests for no remaining nonlinearity, nonnormality, no remaining serial dependency, parameter constancy
various plots to check estimated model


Nonparametric Analysis

lag selection for univariate models based on linear and nonlinear selection criteria
nonlinear estimation with configurable 3D plots
residual analysis
model selection for volatility process
estimation of volatility process
residual analysis for volatility estimation residuals


ARIMA Analysis with fixed regressors (univariate)

lag selection for AR and MA parameters with Hannan-Rissanen procedure
estimation with fixed regressors
residual analysis
ARCH modelling of residuals
forecasting with fixed regressors

Download JMulTi 4.24