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.
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.
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.
The new GAUSS Machine Learning (GML) library offers powerful and efficient machine learning techniques in an accessible and friendly environment. Whether you’re just getting familiar with machine learning or an experienced technician, you’ll be running models in no time with GML.
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.
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:
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:
If you’ve explored machine learning models, you’ve most likely encountered the term “cross-validation” at some point. Cross-validation is an important step for training robust and reliable maachine learning models.
In this blog, we’ll break cross-validation into simple terms. Using a practical demonstration, we’ll equip you with the knowledge to confidently use cross-validation in your machine learning projects.
Machine learning algorithms often rely on hyperparameters that can impact the performance of the models. These hyperparameters are external to the data and are part of the modeling choices that practitioners must make.
An important step in machine learning modeling is optimizing model hyperparameters to improve prediction accuracy.
In today’s blog, we will cover some fundamentals of parameter tuning and will look more specifically at fine-tuning our previous decision forest model.
In today’s blog, we compare three different machine learning regression techniques for predicting U.S. real GDP output gap. We will use a combination of common economic indicators and GDP subcomponents to predict the quarterly GDP output gap.
Working with strings hasn’t always been easy in GAUSS. In the past, the only option in GAUSS was to store strings separately from numeric data. It made it difficult to work with datasets that contained mixed types.
With the introduction of GAUSS dataframes in GAUSS 21 and the enhanced string capabilities of GAUSS 23, that has all changed! I would argue that GAUSS now offers one of the best environments for managing and cleaning mixed-type data.
I recently used GAUSS to perform the very practical task of creating an email list from a string-heavy dataset – something I never would have chosen GAUSS for in the past. In this blog, we walk through this data cleaning task, highlighting several key features for handling strings.
