Introduction
With over 40 new features, enhancements, and bug fixes, Time Series MT (TSMT) 4.0 is s one of our most significant updates yet.
Highlights of the new release include:
- Structural VAR (SVAR) Tools.
- Enhanced SARIMA Modeling.
- Extended Model Diagnostics and Reporting.
- Seamless Dataframe Integration.
New SVAR Tools
// Declare control structure // and fill with defaults struct svarControl ctl; ctl = svarControlCreate(); ctl.irf.ident = "long"; // Set maximum number of lags maxlags = 8; // Turn constant on const = 1; // Check structural VAR model call svarFit(Y, maxlags, const, ctl);
TSMT 4.0 includes a new comprehensive suite of no-hassle functions for intuitively estimating SVAR models.
- Effortlessly estimate reduced-form parameters, impulse response functions (IRFs), and forecast error variance decompositions (FEVDs) using svarFit.
- Take advantage of built-in identification strategies, including Cholesky decomposition, sign restrictions, and long-run restrictions.
- Use new functions for cleanly plotting IRFs and FEVDs.
Enhanced SARIMA Modeling
Significant upgrades to the SARIMA state space framework deliver improved numerical stability, more accurate covariance estimation, and rigorous enforcement of stationarity and invertibility conditions.
Key enhancements include:
- Simplified Estimations: Optional arguments with smart defaults streamline model setup and estimation.
- Broader Model Support: Support now includes white noise and random walk models with optional constants and drift terms.
- Enhanced Accuracy: Standard errors are now computed using the delta method, explicitly accounting for constraints that enforce stationarity and invertibility.
Extended Model Diagnostics and Reporting
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Model: ARIMA(1,1,1) Dependent variable: wpi
Time Span: 1960-01-01: Valid cases: 123
1990-10-01
SSE: 64.512 Degrees of freedom: 121
Log Likelihood: 369.791 RMSE: 0.724
AIC: 369.791 SEE: 0.730
SBC: -729.958 Durbin-Watson: 1.876
R-squared: 0.449 Rbar-squared: 0.440
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Coefficient Estimate Std. Err. T-Ratio Prob |>| t
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AR[1,1] 0.883 0.063 13.965 0.000
MA[1,1] 0.420 0.121 3.472 0.001
Constant 0.081 0.730 0.111 0.911
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Completely redesigned output reports and extended diagnostics make model evaluation and comparison easier and more insightful than ever.
New enhancements include:
- Expanded diagnostics for quick assessment of model fit and underlying assumptions.
- Clear, intuitive reports that make it easy to compare multiple models side-by-side.
- Improved readability, to help identify key results and insights.
Full Dataframe Integration
// Lag of independent variables lag_vars = 2; // Autoregressive order order = 3; // Call autoregmt function call autoregFit(__FILE_DIR $+ "autoregmt.gdat", "Y ~ X1 + X2", lag_vars, order);
Complete compatibility with GAUSS dataframes, simplifies the modeling workflow and ensures outputs are intuitive and easy to interpret.
- Automatic Variable Name Recognition: Automatically detects and uses variable names, eliminating manual setup and saving time.
- Effortless Date Management: Intelligent handling of date formats and time spans for clearer output reports.
- Clear, Interpretable Outputs: Results are clearly labeled and easy to follow, helping boost productivity and reduce confusion.