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.
While structural breaks are a widely examined topic in pure time series, their impacts on panel data models have garnished less attention.
However, in their forthcoming paper Chowdhury and Russell (2018)]
demonstrate that structural breaks
can cause bias in the instrumental variable panel estimation framework.
This work highlights that structural breaks shouldn’t be limited to pure time series models and warrant equal attention in panel data models.