Panel data econometrics is a specific form of statistical data analysis. It involves multi-dimensional data, which is where the data measures multiple things for the same subject. This naturally allows analysts to find more information and patterns, including cross-referencing data. The downside of panel data econometrics is that it can be much more complicated to analyze.
Econometrics is an activity that lies somewhere between economics and statistics. Much of traditional economics involves developing theories to explain and predict activities such as market behavior. Econometrics is more about starting with results and attempting to work backward to find possible causes and connections.
Panel data is sometimes known as longitudinal data — it is any set of data that covers multiple factors for the same subjects. For example, a list of the height of every child in a class would be ordinary data. A list of every child in a class giving both the child's height and the child's weight would be a very simple form of panel data. Some forms of panel data are far more complicated: for example, a national census may contain dozens of items of data about each household.
At its simplest, panel data econometrics can be used to establish relationships. For example, a set of data may show the college admission test scores of former students and their salaries ten years after leaving school. This could show a strong relationship between having a high score and having a high salary. This does not necessarily prove the two are connected: a commonly used phrase is that "correlation does not equal causation."
More complex panel data econometrics can work with multiple factors. For example, the test scores and salary data might also include details of the average test score in the student's school. By cross-referencing, analysts could find the salaries are more dependent on how well a student performed compared with his or her classmates than in the student's actual score. This could lead to a theory that students who outperform peers are more competitive or driven and that this translates into getting ahead in the workplace and winning promotions.
Using multiple variables can make it easier to identify potential links. It can also reduce the chances that a particular link was caused purely by chance, or make it clearer when that is the case. The main problem is that each additional variable causes a dramatic increase in the total number of potential links being explored. This not only increases the analysis work required, but increases the chance of a mistake slipping through.