 # Recent Posts

## Update Discrete Choice Application Module

Introduction The latest Discrete Choice Analysis Tools 2.1.0 is now available for release. If you own Discrete Choice 2.0 the update is available for free. New features include tools for computing: Average marginal effects (AME) Marginal effects at the mean (MEM). Change Log Added ability to compute average marginal effects. Added error checking for variable [...]

## GAUSS Basics 7: Conditional statements

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. Previous: GAUSS Basics 6: Logical and relational operators
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## Fundamental Bayesian Samplers

Introduction 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. We can, however, start to build a better understanding of sampling by examining three [...]

## The Current Working Directory: What you need to know

Introduction Whether you are new to GAUSS, or have been around for a while, today's blog will have something for you. We'll answer the questions: What is the current working directory in GAUSS? How can I find my working directory? How can I change my working directory? Then we'll show you how some common GAUSS [...]

## GAUSS Basics 6: Logical and relational operators

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 You will also see these operators used to select specific rows of a matrix with logical indexing. Next: GAUSS Basics 7: Conditional statements Previous: GAUSS Basics 5: Element-by-element [...]

## Marginal Effects of Linear Models with Data Transformations

Introduction 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. Marginal effects measure the impact that an instantaneous unit change in one variable has on the outcome variable while all other [...]

## GAUSS Basics 5: Element-by-element conformability

Learn how the GAUSS element-by-element conformability rules help you to create code which is compact, elegant and fast! Applies to functions as well as matrices and vectors. Avoids the need for loops in many cases. Important concepts to help you get the most from GAUSS. Next: GAUSS Basics 6: Logical and relational operators Previous: GAUSS [...]
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## Panel Data Basics: One-way Individual Effects

Introduction In this blog, we examine one of the fundamentals of panel data analysis, the one-way error component model. Today we will: Explain the theoretical one-way error component model. Consider fixed effects vs. random effects. Estimate models using an empirical example. The theoretical one-way error component model The one-way error-component model is a panel data [...]

## GAUSS Basics 4: Matrix operations

This fourth video in our GAUSS Basics series will explain how to perform: Matrix operations. Element-by-element operations on matrices. Next: GAUSS Basics 5: Element-by-element conformability Previous: GAUSS Basics 3: Introduction to matrices
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## Introduction to Difference-in-Differences Estimation

Introduction 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 order to truly know how those individuals have been impacted, we need to consider how those individuals would be [...]

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