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.
When policy changes or treatments are imposed on people, it is common and reasonable to ask how those people have been impacted. This is a more difficult question than it seems at first glance. In today’s blog, we examine difference-in-differences (DD) estimation, a common tool for considering the impact of treatments on individuals.
Learn how to work with matrices, the building block of the GAUSS programming language, in this third video in our GAUSS Basics series. Today we will explore how to:
GAUSS includes a plethora of tools for creating publication-quality graphics. Unfortunately, many people fail to use these tools to their full potential. Today we unlock five advanced GAUSS hacks for building beautiful graphics:
Using HSL, and Colorbrewer color palettes.
Controlling graph exports.
Changing the plot canvas size.
Annotating graphs with shapes, text boxes, and lines.
Using LaTeX for GAUSS legends, labels and text boxes.
