Panel Data Analysis
Panel data is also called longitudinal data. It deals with data that has observations about different cross-sections across time. Groups that can make up panel data series include countries, individuals, firms, demographic groups, etc. Panel data is just like time-series data. It contains observations collected at a regular chronology and frequency. Also, just like cross-sectional data, panel data contains observations across a collection of individuals.
What are the advantages of panel data?
- It can model both individual behavior and common behaviors of groups
- It is more variable and efficient than pure cross-sectional and time-series data
- It can measure and detect statistical effects. Pure time-series and cross-sectional data cannot do this
- Panel data minimizes estimation biases that arise from aggregating groups into a single time-series
Panel data is used in a wide range of fields including medicine, economics, social science, physical sciences, epidemiology, medicine, finance, etc.
We can also define panel data as a collection of quantities obtained from multiple individuals. This data is assembled over even intervals in time and ordered chronologically. Our help with panel data analysis assignment covers all topics in this subject. Get in touch with us whenever you need assistance.
Long and wide panel datasets
Panel data can be stored in different formats:
- Long format datasets – stack the observations of each variable from all groups across all time into one column
- Wide-format data sets – Panel data is stored with the observations for a single variable from separate groups stored in separate columns.
Balanced and unbalanced panel data
Panel data can also be classified as balanced and unbalanced.
- Balanced panel datasets – They have the same number of observations for all groups
- Unbalanced panel datasets – They have missing values for some time observations for some of the groups
Balanced datasets only apply to certain panel data models. Also, unbalanced panel datasets may need to be condensed to include only the consecutive period for which there are observations for all individuals in the cross-section.
Heterogeneity and Panel Data
Panel data analysis focusses on addressing the likely dependence across data observations within the same group. The major difference between panel data analysis and time-series analysis is that panel data models support heterogeneity across groups and introduce individual-specific effects.
For example, suppose we have a panel data series that includes gross domestic product (GDP) data for a panel of 5 different countries: Greece, France, Australia, the United States, and Canada.
- All the countries are likely to be impacted by a worldwide economic recession. This can result in changes in the GDP across all the five countries
- Political unrest in Australia is likely to affect Australia’s GDP but may not affect the other countries in the panel.
- Trade policy changes in North America may only regionally affect the US and Canada
- France and Greece will be affected by a change in the Euro exchange rate
Most panel data models have effective techniques that can address these heterogeneities across individuals. Moreover, cross-sectional and time-series models may not be suitable to handle this heterogeneity.
How panel data is modeled
It is common for researchers to analyze datasets with multiple observations of a set of cross-sectional units over some period. For example, data from the production department of multiple companies or the gross product of several countries across several years.
Panel data series are modeled by a unique branch of time-series modeling. The branch is made up of methodologies specific to their structure. We can split panel data methods into two broad categories:
- Homogenous category – Sometimes called the pooled panel data models. It assumes that the model parameters are common across individuals.
- Heterogeneous category – Allow all or any of the model parameters to vary across individuals. Some of the examples of heterogeneous panel data models include fixed effects and random-effects models.
The assumptions made about the variations of the model within these groups are the primary drivers behind the use of the model.
Dynamic Panel Data Model
The modeling of dynamics using lagged dependent variables is a critical component of pure time-series models. The autocorrelation between observations of the same dataset at different points in time is captured by these lagged variables. It is essential to address the possibility of autocorrelation in panel data because panel datasets include a time-series component. Dynamics is added to the panel data individual effects framework by the dynamic panel data model. Arellano and Bond (1991) proposed the generalized methods of moments (GMM) framework which is commonly used to estimate the dynamic panel data models.
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