Data analysis is a crucial step after constructing an econometric model. We need datasets to gather evidence supporting or refuting our hypotheses. There are various types of economic data sets but we will describe the types of data sets in econometrics which are considered as a key in our practical scenarios. Let’s start our discussion.
Types of Data Sets in Econometrics
In this article, we will discuss the four major types of data: cross-sectional, time series, panel, and pooled data.
Cross-Sectional Data
A cross-sectional dataset provides information of multiple individuals/members/observations at a specific point in time. It comprises a sample of individuals, households, firms, countries, and other units at a particular time, such as in the year 20## or in January.
Example of Cross-Sectional Dataset: Wages of employees in the ABC industry in January.
This is an example of cross-sectional data because it contains information on wages from individual entities (employees) at a specific point in time (January).
Time Series Data
Time series data consists of observations on single or multiple variables over time. It assumes that past events influence future events, allowing us to use past data to forecast or estimate future occurrences.
Example of Time Series Data: Average share price of ABC company from 2000 to 2024.
In this example, we have only one variable (Stock Price) and one entity (ABC Company) but data spanning many years (from 2000 to 2024).
Pooled Cross Sections
Pooled cross-sections combine features of both cross-sectional and time series datasets. If we have cross-section data of random samples at one point in time and another cross-section data of random samples at a different point in time, the combination of the two datasets is called a pooled cross-section dataset. Importantly, the observations or individuals in the samples should differ over time.
Example of Pooled Cross Sections: In 2023, we collected wage data of workers from ABC Country selected from a random sample. The following year in 2024, we collected wage data of workers from ABC Country selected from another NEW random sample. The combination of these two datasets is a pooled cross-section dataset.
Panel or Longitudinal Dataset
Panel data also incorporates features of both cross-sectional and time series datasets but differs from pooled cross-sections. Panel data consists of time series data for each member, observation, or individual in cross-sectional data. Unlike pooled cross-sections, it should have the same individual or observation over time.
Example of Panel or Longitudinal Dataset: Wage data of workers A, B, and C from a Country from the year 2015 to 2024 is an example of panel data. In this example, we have data from 2015 to 2024 (time series) and data from the same members A, B, and C (cross-sectional).
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Conclusion
In conclusion, the aforementioned four types are the most common and frequently used types of data sets in econometrics. Each dataset requires different methodologies for analysis.
FAQs
What are the four data structures in econometrics?
Among the four data structures in econometrics, cross-sectional and time series are the major types, while pooled cross-sections and panel or longitudinal are hybrid types that share features of the major two.
