Category: Econometrics

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.

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.

Principal components analysis (PCA) is a useful tool that can help practitioners streamline data without losing information. In today’s blog, we’ll examine the use of principal components analysis in finance using an empirical example. We’ll look more closely at:

• What PCA is.
• How PCA works.
• How to use the GAUSS Machine Learning library to perform PCA.
• How to interpret PCA results.

Forecasts have become a valuable commodity in today’s data-driven world. Unfortunately, not all forecasting models are of equal caliber, and incorrect predictions can lead to costly decisions. Today we will compare the performance of several prediction models used to predict recessions. In particular, we’ll look at how a traditional baseline econometric model compares to machine learning models. Our models will include:

• A baseline probit model.
• K-nearest neighbors.
• Decision forests.
• Ridge classification.