Erica has been working to build, distribute, and strengthen the GAUSS universe since 2012. She is an economist skilled in data analysis and software development. She has earned a B.A. and MSc in economics and engineering and has over 15 years combined industry and academic experience in data analysis and research.
Introduction You're probably familiar with the basic find-and-replace. However, large projects with many files across several directories, require a more powerful search tool. The GAUSS Source Browser is the powerful search-and-replace tool you need. We'll discuss some ways you can use it to make your life easier, including: How to find all usages of a [...]
Introduction Panel data, sometimes referred to as longitudinal data, is data that contains observations about different cross sections across time. Examples of groups that may make up panel data series include countries, firms, individuals, or demographic groups. Like time series data, panel data contains observations collected at a regular frequency, chronologically. Like cross-sectional data, panel [...]
Introduction The aggregate function, first available in GAUSS version 20, computes statistics within data groups. This is particularly useful for panel data. In today's blog, we take a closer look at aggregate. We will: Introduce the basics of the aggregate function. Explain how to use the aggregate function Demonstrate a real-world application of the aggregate [...]
Introduction 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 [...]
Introduction Time series data is data that is collected at different points in time. This is opposed to cross-sectional data which observes individuals, companies, etc. at a single point in time. Because data points in time series are collected at adjacent time periods there is potential for correlation between observations. This is one of the [...]
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 tspdlib 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 [...]
Introduction 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 What is a GAUSS package? A [...]
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 [...]
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 [...]
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 [...]
