Panel data offers a unique opportunity to examine both individual-specific and time-specific effects. However, as anyone who has worked with panel data knows, these same features that make panel data so useful can also make exploration and cleaning particularly challenging.
GAUSS 25 was designed with these challenges in mind. It introduces a comprehensive new suite of panel data tools, tailored to make working with panel data in GAUSS easier, faster, and more intuitive.
In today’s blog, we’ll look at these new tools and demonstrate how they can simplify everyday panel data tasks, including:
In this video, you’ll learn the basics of panel data analysis in GAUSS. We demonstrate panel data modeling start to finish, from loading data to running a group specific intercept model.
Anyone who works with panel data knows that pivoting between long and wide form, though commonly necessary, can still be painstakingly tedious, at best. It can lead to frustrating errors, unexpected results, and lengthy troubleshooting, at worst.
The new dfLonger and dfWider procedures introduced in GAUSS 24 make great strides towards fixing that. Extensive planning has gone into each procedure, resulting in comprehensive but intuitive functions.
In today’s blog, we will walk through all you need to know about the dfLonger procedure to tackle even the most complex cases of transforming wide form panel data to long form.
When they’re done right, graphs are a useful tool for telling compelling data stories and supporting data models. However, too often graphs lack the right components to truly enhance understanding.
In this blog, we look at how a few quick customizations help make graphs more impactful. In particular, we will consider:
Using grid lines without cluttering a graph.
Changing tick labels for readability.
Using clear axis labels.
Marking events and outcomes with lines, bars, and annotations.
Reliable unit root testing is an important step of any time series analysis or panel data analysis.
However, standard time series unit root tests and panel data unit root tests aren’t reliable when structural breaks are present. Because of this, when structural breaks are suspected, we must employ unit root tests that properly incorporate these breaks.
Today we will examine one of those tests, the Carrion-i-Silvestre, et al. (2005) panel data test for stationarity in the presence of multiple structural breaks.
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.
In this blog, we examine one of the fundamentals of panel data analysis, the one-way error component model. We cover the theoretical background of the one-way error component model, we examine the fixed-effects and random-effects models, and provide an empirical example of both.
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.
In this blog, we extend our analysis of unit root testing with structural breaks to panel data. Using panel data unit roots tests found in the GAUSS tspdlib we consider if a panel of international current account balances collectively shows unit root behavior.
