GAUSS 20 Initial Release
This blog introduces the latest additions to GAUSS. Read it today to learn all about the newest GAUSS features for data analysis, now available in GAUSS 20.
How to Conduct Unit Root Tests in GAUSS
In time series modeling we often encounter trending or nonstationary time series data. Understanding the characteristics of such data is crucial for developing proper time series models. For this reason, unit root testing is an essential step when dealing with time series data. In this blog post, we cover everything you need to conduct time series data unit root tests using GAUSS.
Introduction to the Fundamentals of Time Series Data and Analysis
The statistical characteristics of time series data often violate the assumptions of conventional statistical methods. Because of this, analyzing time series data requires a unique set of tools and methods, collectively known as time series analysis. This article covers the fundamental concepts of time series analysis and should give you a foundation for working with time series data. Everything is covered from time series plotting to time series modeling.
How to mix, match and style different graph types
Often times we need to mix multiple graph types in order to create a plot which most effectively tells the story of our data. In this post, we will create a plot of the Phillips Curve in the United States over two separate time periods. We will show how to add scatter points and lines as well as data series’ of different lengths to a single plot. However, our main focus will be showing you how to control the styling of all aspects of the plot in these cases.
New release of tspdlib 1.0
The preliminary econometric package for Time Series and Panel Data Methods has been updated and functionality has been expanded in this first official release of tspdblib 1.0. The tspdlib 1.0 package includes functions for time series unit root tests in the presence of structural breaks, time series and panel data unit root tests in the presence of structural breaks, and panel data causality tests. It is available for direct installation using the GAUSS Package Manager.
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
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
Tagged in
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.