Author: Eric

Introducing the GAUSS Data Management Guide

If you’ve worked with real-world data, you know that data cleaning and management can eat up your time. Efficiently tackling tedious data cleaning, organization, and management tasks can have a huge impact on productivity.
We created the **GAUSS Data Management Guide** with that exact goal in mind. It’s aimed to help you save time and make the most of your data.
Today’s blog looks at what the GAUSS Data Management Guide offers and how to best use the guide.

Using Feasible Generalized Least Squares To Improve Estimates

Data analysis in reality is rarely as clean and tidy as it is presented in the textbooks. Consider linear regression — data rarely meets the stringent assumptions required for OLS. Failing to recognize this and incorrectly implementing OLS can lead to embarrassing, inaccurate conclusions. In today’s blog, we’ll look at how to use feasible generalized least squares to deal with data that does not meet the OLS assumption of Independent and Identically Distributed (IID) error terms.

Getting Started With Survey Data In GAUSS

Survey data is a powerful analysis tool, providing a window into people’s thoughts, behaviors, and experiences. By collecting responses from a diverse sample of responders on a range of topics, surveys offer invaluable insights. These can help researchers, businesses, and policymakers make informed decisions and understand diverse perspectives.
In today’s blog we’ll look more closely at survey data including:
  • Fundamental characteristics of survey data.
  • Data cleaning considerations.
  • Data exploration using frequency tables and data visualizations.
  • Managing survey data in GAUSS.

Transforming Panel Data to Long Form in GAUSS

Anyone who works with panel data knows that pivoting between long and wide form, though commonly necessary, can still be painstakingly tedious, at best. It can lead to frustrating errors, unexpected results, and lengthy troubleshooting, at worst.
The new dfLonger and dfWider procedures introduced in GAUSS 24 make great strides towards fixing that. Extensive planning has gone into each procedure, resulting in comprehensive but intuitive functions.
In today’s blog, we will walk through all you need to know about the dfLonger procedure to tackle even the most complex cases of transforming wide form panel data to long form.

Introducing GAUSS 24

We’re happy to announce the release of GAUSS 24, with new features for everything from everyday data management to refined statistical modeling. GAUSS 24 features a robust suite of tools designed to elevate your research. With these advancements, GAUSS 24 continues our commitment to helping you conduct insightful analysis and achieve your goals.

New Release TSPDLIB 3.0

The preliminary econometric package for Time Series and Panel Data Methods has been updated and functionality has been expanded with over 20 new functions in this release of TSPDLIB 3.0.0. The TSPDLIB 3.0.0 package includes expanded functions for time series and panel data testing both with and without structural breaks and causality testing. It requires a GAUSS 23+ for use.

Classification with Regularized Logistic Regression

Logistic regression has been a long-standing popular tool for modeling categorical outcomes. It’s widely used across fields like epidemiology, finance, and econometrics. In today’s blog we’ll look at the fundamentals of logistic regression. We’ll use a real-world survey data application and provide a step-by-step guide to implementing your own regularized logistic regression models using the GAUSS Machine Learning library, including:
  1. Data preparation.
  2. Model fitting.
  3. Classification predictions.
  4. Evaluating predictions and model fit.

Machine Learning With Real-World Data

If you’ve ever done empirical work, you know that real-world data rarely, if ever, arrives clean and ready for modeling. No data analysis project consists solely of fitting a model and making predictions. In today’s blog, we walk through a machine learning project from start to finish. We’ll give you a foundation for completing your own machine learning project in GAUSS, working through:
  • Data Exploration and cleaning.
  • Splitting data for training and testing.
  • Model fitting and prediction.

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