Announcing Time Series MT 4.0

by Eric · Published June 18, 2025

Introduction

We’re excited to share the official release of Time Series MT (TSMT) 4.0!

This release provide a major upgrade to our GAUSS time series tools. With over 40 new features, enhancements, and improvements, TSMT 4.0 significantly expanding the scope and usability of TSMT.

With the TSMT 4.0 library, you can run SVAR models out of the box, without complicated programming. Easy to use new features allow you to:

• Estimate reduced-form VAR parameters, impulse response functions (IRFs), and forecast error variance decompositions (FEVDs) with ease.
• Apply built-in identification strategies like Cholesky decomposition, sign restrictions, and long-run restrictions.
• Visualize results using new, streamlined functions for plotting IRFs and FEVDs.

TSMT 4.0 makes complex SVAR analysis more accessible—without sacrificing analytical rigor.

SARIMA Modeling: Now Smarter and More Flexible

SMT 4.0 delivers a complete overhaul of its SARIMA modeling capabilities, bringing you:

• Enhanced numerical stability and robust covariance estimation.
• Intelligent enforcement of stationarity and invertibility conditions.
• Simplified estimation with smart defaults and fewer required inputs.
• Support for special cases like white noise and random walks, with or without drift.
• Accurate standard error estimation via the delta method.

These upgrades streamline SARIMA modeling and help ensure more reliable results across a wider range of model structures.

More Insightful Model Diagnostics and Reporting

================================================================================
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
================================================================================
Coefficient                Estimate      Std. Err.        T-Ratio     Prob |>| t
================================================================================

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
================================================================================

We’ve reimagined the output experience in TSMT 4.0, making it easier to interpret and compare model results:

• Output reports are now cleaner, clearer, and more informative.
• Expanded diagnostics help you quickly evaluate model assumptions and performance.
• Built-in summaries make it simple to assess multiple models side-by-side.

With TSMT 4.0, you’ll spend less time deciphering output and more time drawing insights.

Seamless Integration with GAUSS Dataframes

library tsmt;
 
// Load dataframe
fname = getGAUSSHome("pkgs/tsmt/examples/var_enders_trans.gdat");
data = loadd(fname);
 
// Estimate the model
call varmaFit(data, "spread + d_lip_detrend + d4_unem", 3);

TSMT 4.0 fully embraces the GAUSS dataframe ecosystem, offering:

• Automatic recognition of variable names and time spans.
• No manual reformatting required, just load your time series data and go.
• Outputs that automatically interpret dates and provide human-readable labeling.

This integration minimizes setup time and boosts productivity, especially when working with large or complex datasets.

Try Out The GAUSS Time Series MT 4.0 Library