Econometrics | Aptech

Jan 11 by Eric Posted in Econometrics

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.

Dec 12 by Eric Posted in Econometrics, Panel data, Programming

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.

Aug 28 by Eric Posted in Econometrics, Machine learning, Releases

Announcing the GAUSS Machine Learning Library

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.

Jun 07 by Eric Posted in Econometrics, Machine learning

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:

Data preparation.
Model fitting.
Classification predictions.
Evaluating predictions and model fit.

May 15 by Eric Posted in Econometrics, Machine learning

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.

Mar 15 by Eric Posted in Econometrics, Machine learning

Applications of Principal Components Analysis in Finance

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.

Feb 21 by Eric Posted in Econometrics, Machine learning

Predicting Recessions with Machine Learning Techniques

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.

Jan 17 by Eric Posted in Econometrics

The Fundamentals of Kernel Density Estimation

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.

Sep 14 by Jamel Posted in Econometrics, Time Series

Addressing Conditional Heteroscedasticity in SVAR Models

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.

Sep 05 by Eric Posted in Econometrics, Time Series

Unobserved Components Models; The Local Level Model

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.

Category: Econometrics