Announcing Time Series MT 4.0
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.
Easier ARIMA Modeling with State Space: Revisiting Inflation Modeling Using TSMT 4.0
Estimate ARIMA models in state space form using GAUSS. Learn how arimaSS simplifies modeling, automates forecasting, and supports lag selection.
Sign Restricted SVAR in GAUSS
In structural vector autoregressive (SVAR) modeling, one of the core challenges is identifying the structural shocks that drive the system’s dynamics.
Traditional identification approaches often rely on short-run or long-run restrictions, which require strong theoretical assumptions about contemporaneous relationships or long-term behavior.
Sign restriction identification provides greater flexibility by allowing economists to specify only the direction, positive, negative, or neutral, of variable responses to shocks, based on theory.
In this blog, we’ll show you how to implement sign restriction identification using the new GAUSS procedure, **svarFit**, introduced in TSMT 4.0.
Traditional identification approaches often rely on short-run or long-run restrictions, which require strong theoretical assumptions about contemporaneous relationships or long-term behavior.
Sign restriction identification provides greater flexibility by allowing economists to specify only the direction, positive, negative, or neutral, of variable responses to shocks, based on theory.
In this blog, we’ll show you how to implement sign restriction identification using the new GAUSS procedure, **svarFit**, introduced in TSMT 4.0.
Estimating SVAR Models With GAUSS
Structural Vector Autoregressive (SVAR) models provide a structured approach to modeling dynamics and understanding the relationships between multiple time series variables. Their ability to capture complex interactions among multiple endogenous variables makes SVAR models fundamental tools in economics and finance. However, traditional software for estimating SVAR models has often been complicated, making analysis difficult to perform and interpret. In today’s blog, we present a step-by-step guide to using the new GAUSS procedure, svarFit, introduced in TSMT 4.0. We will cover: Estimating reduced form models. Structural identification using short-run restrictions. Structural identification using long-run restrictions. Structural identification using sign restrictions.
Why You Should Consider Constrained Maximum Likelihood MT (CMLMT)
The Constrained Maximum Likelihood (CML) library was one of the original constrained optimization tools in GAUSS. Like many GAUSS libraries, it was later updated to an “MT” version.
The “MT” version libraries, named for their use of multi-threading, provide significant performance improvements, greater flexibility, and a more intuitive parameter-handling system.
This blog post explores:
- The key features, differences, and benefits of upgrading from CML to CMLMT.
- A practical example to help you transition code from CML to CMLMT.
Exploring Categorical Data in GAUSS 25
Categorical data plays a key role in data analysis, offering a structured way to capture qualitative relationships. Before running any models, simply examining the distribution of categorical data can provide valuable insights into underlying patterns.
In GAUSS 25, these functions received significant enhancements, making them more powerful and user-friendly. In this post, we’ll explore these improvements and demonstrate their practical applications.
Whether summarizing survey responses or exploring demographic trends, fundamental statistical tools, such as frequency counts and tabulations, help reveal these patterns.
Hypothesis Testing In GAUSS
If you’re an applied researcher, odds are (no pun intended) you’ve used hypothesis testing. Hypothesis testing is an essential part of practical applications, from validating economic models, to assessing policy impacts, to making informed business and financial decisions.
The usefulness of hypothesis is its ability to provide a structured framework for making objective decisions based on data rather than intuition or anecdotal evidence. It provides us a data-driven method to check the validity of our assumptions and models. The intuition is simple — by formulating null and alternative hypotheses, we can determine whether observed relationships between variables are statistically significant or simply due to chance.
In today’s blog we’ll look more closely at the statistical intuition of hypothesis testing using the Wald Test and provide a step-by-step guide for implementing hypothesis testing in GAUSS.
Get Started with Panel Data in GAUSS (Video)
In this video, you’ll learn the basics of panel data analysis in GAUSS. We demonstrate panel data modeling start to finish, from loading data to running a group specific intercept model.
New Video! Get Started with Choice Modeling in GAUSS
In this video, you’ll learn the basics of choice data analysis in GAUSS. Our video demonstration shows just how quick and easy it is to get started with everything from data loading to discrete data modeling.