Features

General Remarks

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. For an overview of the underlying software concept, see the JStatCom page.

Screenshots

Some images of JMulTi in action are captured here.

• VECM Impulse Response Analysis with bootstrapped Efron and Hall confidence intervals
• Johansen Trace test, p-values computed with Doornik's method
• VECM Estimation with output in matrix form

Econometric Features

JMulTi comes with a comprehensive help system that has been generated with JHelpDev.

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