Category: Econometrics

Hypothesis Testing In GAUSS

If you’re an applied researcher, odds are (no pun intended) you’ve used hypothesis testing. Hypothesis testing is an essential part of practical applications, from validating economic models, to assessing policy impacts, to making informed business and financial decisions. The usefulness of hypothesis is its ability to provide a structured framework for making objective decisions based on data rather than intuition or anecdotal evidence. It provides us a data-driven method to check the validity of our assumptions and models. The intuition is simple — by formulating null and alternative hypotheses, we can determine whether observed relationships between variables are statistically significant or simply due to chance. In today’s blog we’ll look more closely at the statistical intuition of hypothesis testing using the Wald Test and provide a step-by-step guide for implementing hypothesis testing in GAUSS.

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.

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.

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.

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