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.
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.
The GAUSS Package Manager, first introduced in version 20, allows you to download, install and uninstall GAUSS packages without leaving GAUSS. It supports the paid GAUSS Application Modules, free GAUSS packages and even allows you to create custom packages and channels. This post will guide you through the basics needed to install and uninstall GAUSS packages.
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.
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.
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.
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.
