| • | Estimate VAR models using Maximum Likelihood or analyse them based on Bayesian methods. |
| • | Perform cointegration analysis. |
| • | Estimate α, β and C under general linear restrictions. |
| • | Estimate structural VARs with identifying restrictions on the contemporaneous and/or long-run effects of shocks. |
| • | Allows for over-identifying restrictions on the structural model including over-identifying long-run restrictions. |
| • | Can estimate all parameters of the system simultaneously. |
| • | Can bootstrap over-identification tests and parameters for Structural VARs including impulse response functions. |
| • | Can forecast levels and growth series of the endogenous variables and functions of linear combinations of these. |
| • | Highly configurable with a large selection of options. |
| • | Uses model files which allows fast switching between various basic specifications. |
| • | Compute a wide range of specification tests if asked to do so! |
| • | Import data from text files, MatLab MAT files, Lotus 1-2-3 spreadsheets, and Excel spreadsheets (under either MatLab 6.x or later, or for a stand-alone executable when it has been compiled using MatLab's compiler for MatLab 6.x or later). |
| • | Export data to text files from lines and patches (confidence bands and histograms) in the stand-alone release. |
| • | Allows for partial systems. |
| • | Compute Bartlett corrected trace tests. |
| • | Can compute p-values for trace tests. |
| • | Distributions for trace test in partial systems are included for a maximum of 12 endogenous variables and 11 exogenous I(1) variables. |
| • | For partial systems with only exogenous I(0) regressors, p-values and critical values for the trace test are provided in models no additional deterministics. That is, models with restricted and/or unrestricted constant and/or linear trends as well as with or without centered seasonal dummies are covered. The approach has been suggested by Boswijk and Doornik (2005) and relies on using the gamma distribution with the appropriate mean and variance. |
| • | The distributions for the trace test with or without I(1) exogenous variables can be simulated from within SVAR. |
| • | Can bootstrap LR trace and Bartlett corrected LR trace tests for the cointegration rank using either "parametric" (draw normalized residuals from a Gaussian distribution) or "non-parametric" (draw new residuals from the estimated residuals using a uniform distribution over the estimated residuals) construction of bootstrapped series. |
| • | Can bootstrap parametrically and non-parametrically the free parameters of the cointegration vectors once these have been identified as well as the LR based test for the restrictions on the cointegration space. |
| • | SVAR can now bootstrap all parameters on the short-run dynamics, i.e., α (on cointegration relations), δ (deterministic variables), Γ (lagged endogenous variables in first differences), Ψ (exogenous I(1) variables in first differences), and Φ (exogenous I(0) variables). If the model contains restrictions on α, the test statistic for these restrictions can also be bootstrapped from the same dialog. |
| • | Compute lag order, weak exogeneity (with respect to α, β), Granger non-causality, and common cycle tests. |
| • | Can bootstrap specification tests, weak exogeneity, Granger causality, lag order tests. |
| • | Compute various formal parameter constancy (e.g. Nyblom and fluctuation) tests and display them graphically. |
| • | The distributions of the Nyblom mean and supremum tests can be simulated with SVAR. |
| • | The asymptotic distributions of the Nyblom mean and supremum tests are included for the models with a restricted constant, with an unrestricted constant, with a restricted linear trend, or with an unrestricted linear trend. |
| • | Can bootstrap the Nyblom test, the fluctuation tests for the non-zero eigenvalues and the Ploberger, Krämer and Kontrus fluctuation tests for the non-cointegration parameters both parametrically and "non-parametrically". Bootstraps can be performed based on pseudo-data generated using the original model or an alternative model (power). Monte Carlo bootstraps can also be calculated (see Nyblom Distribution). |
| • | Bootstrap exercises for the Nyblom tests allow for power studies (using a fixed alternative) as well as Monte Carlo bootstraps. |
| • | Residual analysis with graphics. |
| • | Display cointegration relations and permanent and transitory components based on Beveridge-Nelson decomposition. |
| • | Compute generalized impulse responses (cf. Pesaran and Shin, 1998) for the levels and the cointegration relations along with asymptotic confidence bands and display them graphically. |
| • | Display historical forecast error decompositions. |
| • | Variance decompositions for structural models. |
| • | Graphs of the estimated free coefficients in the identified cointegration space. These graphs are given in 2D and 3D format, with 1 or 2 β coefficients graphed in a region around the maximum against the value of the log-likelihood function. |
| • | Quick view of estimated parameters and various test results. |
| • | Save estimated time series into files. |
| • | Model selection help function: use information criteria for combinations of lag order and rank (if cointegration analysis is selected). |
| • | A rich set of graphics editing tools including data editing. |
| • | Save graphics into a wide range of file formats; the stand-alone release relies heavily on ghostscript and pstoedit (see Supported Graphics). |
| • | The output file can either be produced as formatted plain text or formatted LaTeX. In the latter case, SVAR can display previews of the output in dvi (device independent), ps (PostScript), and pdf (Portable Document Format) provided that the needed programs for compiling into and interpreting these formats exists on your hard drive (or network) and SVAR has been informed about their location. |