Updated March 4, 2023
Introduction to Panel Data Analysis
The following article provides an outline for Panel Data Analysis. A panel data is a combination of a time-series data set (such as stock price with respect to date) and a cross-sectional data set (such as the population of the city of the particular year). When cross-sectional data for multiple year or timestamp gets repeated in the data set is known as panel data. Panel data is also known as cross-sectional data with time series or longitudinal time series data in some cases, the data that is extracted or acquire from a dataset generally by performing observations overtime on a large number of cross-sectional data units for eg. Government records, Accounting records, etc.
What is Panel Data Analysis?
Panel data is generally divided into two categories:
1. Balanced Panel Data
When cross-sectional data with time series repeats a patter of itself on a fixed period time interval it is known as balanced panel data. Here we have the same set of data for every fixed period of the time interval.
Example:
We are having time-series data set of the 5 cities for the year 2001 and the same data set of the same 5 cities for the year 2002.
2. Unbalanced Panel Data
When cross-sectional data with time series does not repeat a patter of itself on a fixed period time interval it is known as balanced panel data. In unbalanced panel data, some of the cross-sectional data is missing for a time interval, it does not have the same set of cross-sections, it contains different sets of cross-sections for the different data sets.
Example:
We are having time-series data set of the 5 cities for the year 2001 and the other data set of different 3 cities for the year 2002.
Some key point which makes the difference between panel data and ordinary data.
- Panel data allows control over variables we can not observe or measure or analyzed.
- Variables that change across time but does not change across the group of data sets.
Advantages of Panel Data Analysis
Given below are the advantages mentioned:
- Panel data contains generalized, common, and individual behaviors of data groups.
- Panel data contains additional info, additional variability, and additional properties than statistical knowledge or cross-sectional knowledge.
- Panel data can be found and live applied with math effects that pure statistic or cross-sectional knowledge cannot.
- Panel data will minimize estimation biases which will arise from aggregating groups into one statistic.
- After extracting the data from the different resources The first step that researchers follow is cleaning data and check the quality of panel data.
- Because it is considered as the panel data is already implicitly well arranged by both cross-sectional and time-series variables and get the presence of fixed and/or random effects of data. Otherwise, the data are simply (or physically) arranged in the panel data format but will not be considered as the panel data in an economic analytical sense.
- The most important aspect is consistency in the unit of data analysis or measurement of the data, which says that each observation in a data set is being treated and weighted equally.
- Some requirement seems self-driven but it is often interval by analytical research. If each observation is not equivalent in many senses, any analysis based on such data may not be adequate and reliable.
Steps of Panel Data Analysis
The step is very similar to other data analysis processes, is to describe the dimension of data carefully before analysis. it is often ignored in many data analysis procedures, but this data description plays very vitally, important, and useful for analytics to get ideas about data and analysis strategies, quality and properties. In the panel data analysis and model section should be as follows.
- Clean the data by eliminating or removing the redundant entities and analyze the data by checking if that data is measured or in a reliable and consistent manner.
- If there are different time period intervals is being used in the panel data then try to rearrange or aggregate data to improve consistency and dimension. If there are multiple missing values in data, decide whether you need to go with balanced panel data by ignoring some pieces of usable information or keep all user information in an unbalanced panel data at the rate of methodological and computational complication.
- Observe and inspect the properties of the panel data including the number of entities it contains, the number of time periods which is repeated with the same set of data and interval gap, need to find out the balanced and unbalanced panel information, and fixed versus rotating panel information. After that try to find out the model that is appropriate for those properties values.
- We have to be alert if the long and short panel data is concerned, that has ten thousand time period or maybe more that along with only 3 entity or a short panel data of two years with one thousand entity records.
- Try to segregate and analyze the data at a fixed point of time with a fixed interval for eg use yearly data or monthly data daily data.
How does Panel Data Analysis work?
- Panel data analysis begins with a simpler model, we can try an ordinary least squares model method instead of a fixed or random data model like a one-way effect model instead of a two-way model; instead of an effect model rather than a linear data model; and so on and so forth.
- Do not try to use a complicated model that is not supported by your panel data like poorly organized, discrete data sets long and short panel, unstructured data, etc.
Conclusion
Panel data analysis is a statistical method to analyze two-dimensional panel data. Panel data is a collection of observations (behavior) for multiple subjects (entities) at different time intervals (generally equally spaced). It is also known as called as Cross-sectional Time-series data as it is a combination of Time series data and Cross-sectional data.
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This is a guide to Panel Data Analysis. Here we discuss the introduction, what is panel data analysis? advantages, steps and working respectively. You may also have a look at the following articles to learn more –
