Update Discrete Choice Application Module
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.
GAUSS Basics 7: Conditional statements
This seventh video in the GAUSS Basics series will show you how to use the
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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.
Fundamental Bayesian Samplers
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.
The Current Working Directory: What you need to know
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.
GAUSS Basics 6: Logical and relational operators
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.
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Marginal Effects of Linear Models with Data Transformations
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.
GAUSS Basics 5: Element-by-element conformability
Learn how to create compact, elegant and fast code using the rules of element-by-element conformability in our fifth GAUSS Basics video. These rules:
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- Apply to functions as well as matrices and vectors.
- Will help you avoid the need for loops in many cases.
Panel Data Basics: One-way Individual Effects
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.
GAUSS Basics 4: Matrix operations
This blog in our GAUSS basics video series explains the differences between matrix operations and element-by-element operations in GAUSS.
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Introduction to Difference-in-Differences Estimation
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.