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.
Eric has been working to build, distribute, and strengthen the GAUSS universe since 2012. He is an economist skilled in data analysis and software development. He has earned a B.A. and MSc in economics and engineering and has over 18 years of combined industry and academic experience in data analysis and research.
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.
Time Series MT 4.0
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.
GAUSS 25.0.1 Maintenance Release Now Available
The latest GAUSS 25.0.1 update is available now and is free if you own GAUSS 25!
This maintenance release enhances graphics and panel data functions, expands functionality, and fixes reported bugs.
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.
Making Your GAUSS Plots More Informative: Working with Legends
In data analysis, a well-designed graph can help clarify your insights but a poorly annotated one can confuse and distract your audience. That’s why proper annotation, including legends, is essential to creating effective graphs.
Legends play a crucial role in making graphs more readable by distinguishing between different groups, categories, or data series. A well-placed legend helps ensure that your message comes across clearly.
In this blog, we’ll walk through how to add and customize legends in GAUSS graphics.
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.
Exploring and Cleaning Panel Data with GAUSS 25
Panel data offers a unique opportunity to examine both individual-specific and time-specific effects. However, as anyone who has worked with panel data knows, these same features that make panel data so useful can also make exploration and cleaning particularly challenging.
GAUSS 25 was designed with these challenges in mind. It introduces a comprehensive new suite of panel data tools, tailored to make working with panel data in GAUSS easier, faster, and more intuitive.
In today’s blog, we’ll look at these new tools and demonstrate how they can simplify everyday panel data tasks, including:
- Loading your data.
- Preparing your panel dataset.
- Exploring panel data characteristics.
- Visualizing panel data.
- Transforming your data for modeling.