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.
Dummy variables are a common econometric tool, whether working with time series, cross-sectional, or panel data. Unfortunately, raw datasets rarely come formatted with dummy variables that are regression ready.
In today’s blog, we explore several options for creating dummy variables from categorical data in GAUSS, including:
Creating dummy variables from a file using formula strings.
Creating dummy variables from an existing vector of categorical data.
Creating dummy variables from an existing vector of continuous variables.
In this blog, we will explore how to set up and interpret cointegration results using a real-world time series example. We will cover the case with no structural breaks as well as the case with one unknown structural break using tools from the GAUSS tspdlib library.
Cointegration is an important tool for modeling the long-run relationships in time series data. If you work with time series data, you will likely find yourself needing to use cointegration at some point. This blog provides an in-depth introduction to cointegration and will cover all the nuts and bolts you need to get started.
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. In this blog, you’ll learn more about using the advanced search-and-replace tools in GAUSS to effectively navigate and edit in projects with multiple files and directories.
Panel data, sometimes referred to as longitudinal data, is data that contains observations about different cross sections across time. Panel data exhibits characteristics of both cross-sectional data and time-series data. This blend of characteristics has given rise to a unique branch of time series modeling made up of methodologies specific to panel data structure. This blog offers a complete guide to those methodologies including the nature of panel data series, types of panel data, and panel data models.
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.
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.
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.