JMulTi Time Series Analysis with Java
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JMulTi Tags

Impulse time series chart time series Time series prediction impulse response econometric procedure VAR/VEC modelling econometric preocedure time series database time series compressor time serie analysis Java Bytecode Analysis Java Structural Analysis Java lock analysis Java heap analysis time series analysis view time series time series visualization time series reports

JMulTi Description

JMulTi was originally created 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. 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. Main features: 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 & L?tkepohl test kernel density estimation spectral density plots crossplots autocorrelation analysis 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 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 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 lag selection for univariate models based on linear and nonlinear selection criteria nonlinear estimation with configurable 3D plots residual analysis for estimation residuals model selection for volatility process estimation of volatility process residual analysis for volatility estimation residuals

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