Author: Erica

Using GAUSS Packages [Complete Guide]

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:
  • What is a GAUSS package
  • Where to find GAUSS packages
  • What is included in GAUSS packages
  • How to use GAUSS packages

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.

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.

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.

Five Hacks For Creating Custom GAUSS Graphics

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.

Unit Root Tests with Structural Breaks

In this blog, we examine the issue of identifying unit roots in the presence of structural breaks. We will use the quarterly US current account to GDP ratio to compare results from a number of unit root test found in the GAUSS tspdlib library including the: Zivot-Andrews (1992) unit root test with a single structural break, Narayan and Popp (2010) unit root test with two structural breaks, Lee and Strazicich (2013, 2003) LM tests with one and two structural breaks, Enders and Lee Fourier (2012) ADF and LM tests.

The Basics of Quantile Regression

Classical linear regression estimates the mean response of the dependent variable dependent on the independent variables. There are many cases, such as skewed data, multimodal data, or data with outliers, when the behavior at the conditional mean fails to fully capture the patterns in the data. In these cases, quantile regression provides a useful alternative to linear regression. Today we explore quantile regression and use the GAUSS quantileFit procedure to analyze Major League Baseball Salary data.

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