Longitudinal data, sometimes called panel data, is a collection of repeated observations of the same subjects, taken from a larger population, over some time – and is useful for measuring change. Longitudinal data differs from cross-sectional data because it follows the same subjects over some time, while cross-sectional data samples different subjects (whether individuals, firms, countries, or regions) at each point in time.

Breaking Down Longitudinal Data

Longitudinal data is often used in economic and financial studies because it has several advantages over repeated cross-sectional data. For example, because longitudinal data measures how long events last for, it can be used to see if the same group of individuals remain unemployed during a recession, or whether different individuals are moving in and out of unemployment. This can help determine the factors that most affect unemployment.

Longitudinal analysis can also be used to calculate a portfolio’s value at risk (VaR), using the historic simulation method. This simulates how the value of the current portfolio would have fluctuated over previous time periods, using the observed historical fluctuations of the assets in the portfolio during those times. It provides an estimate of the maximum likely loss over the next time period.

Longitudinal data is also used in event studies to analyze what factors drive abnormal stock returns over time, or how stock prices react to merger and earnings announcements. It can also be used to measure poverty and income inequality by tracking individual households. And because standardized test scores in schools are longitudinal, they can be used to assess teacher effectiveness and other factors affecting student performance.