GAUSS packages provide access to powerful tools for performing data analysis. This guide covers all you need to know to get the most from GAUSS packages including:
This seventh video in the GAUSS Basics series will show you how to use the if, else, elseif and endif keywords to create code with conditional statements.
The video will demonstrate several examples and show a few common errors you might run into.
The posterior probability distribution is the heart of Bayesian statistics and a fundamental tool for Bayesian parameter estimation. Naturally, how to infer and build these distributions is a widely examined topic, the scope of which cannot fit in one blog. In this blog, we examine bayesian sampling using three basic, but fundamental techniques, importance sampling, Metropolis-Hastings sampling, and Gibbs sampling.
Today we cover what the GAUSS working directory is and how to make the most of it. We’ll show you how some common GAUSS functions use your working directory and some of the errors you’re most likely to run into.
In this sixth video in the GAUSS Basics series learn how to use the logical and relational operators in GAUSS. These operators include and, not, or, xor, less-than, less-than or equal, greater-than, greater-than or equal, equal.
We use regression analysis to understand the relationships, patterns, and causalities in data. Often we are interested in understanding the impacts that changes in the dependent variables have on our outcome of interest. However, not all models provide such straightforward interpretations. Coefficients in more complex models may not always provide direct insights into the relationships we are interested in.
In this blog, we look more closely at the interpretation of marginal effects in three types of models:
Purely linear models.
Models with transformations in independent variables.
Models with transformations of dependent variables.
In this blog, we examine one of the fundamentals of panel data analysis, the one-way error component model. We cover the theoretical background of the one-way error component model, we examine the fixed-effects and random-effects models, and provide an empirical example of both.
