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.
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:
- Data preparation.
- Model fitting.
- Classification predictions.
- 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.
Today’s blog looks closely at the fundamentals of kernel density estimation. After reading this blog you should have an understanding of:
- What kernel density estimation is.
- How kernel density estimation works.
- How to perform kernel density estimation in GAUSS.
Structural VAR models are powerful tools in macroeconomic time series modeling. However, given their vast applications, it is important that they are properly implemented to address the characteristics of their underlying data. In today’s blog, we build on our previous discussions of SVAR models to examine the use of SVAR in the special case of conditional heteroscedasticity. We will look more closely at:
- Conditional heteroscedasticity.
- The impacts of conditional heteroscedasticity on SVAR models.
- Estimating structural impulse response functions (SIRF) in the presence of conditional heteroscedasticity.
- An application to the global oil market.
In today’s blog, we explore a simple but powerful member of the unobserved components family – the local level model. This model provides a straightforward method for understanding the dynamics of time series data. This blog will examine:
- Time series decomposition.
- Unobserved components and the local level model.
- Understanding the estimated results for a local level model.
State-space models provide a powerful environment for modeling dynamic systems. Their flexibility has resulted in a wide variety of applications across fields including radar tracking, 3-D modeling, monetary policy modeling, weather forecasting, and more. In this blog, we look more closely at state-space modeling using a simple time series model of inflation. We cover:
- The components of state-space models.
- Representing state-space models in GAUSS.
- Estimating model parameters using state-space models.
In this video, you’ll learn the basics of time series analysis in GAUSS. See how quick and easy it is to get started with everything from data loading to ARIMA analysis! You’ll see first hand how to :
- Load and verify time series data.
- Filter observations by date.
- Merge data from different sources.
- Create basic time series plots.
- Perform stationarity testing.
- Fit a basic ARIMA model.