Panel data analysis
Panel data analysis is a statistical technique used in econometrics, epidemiology, and social sciences to analyze two dimensional panel data. Panel data allows us to control variables that we cannot measure or observe like the difference in business practices, cultural factors, etc. over time. Examples of groups that could make up panel data include:
- Demographic groups
Panel data is similar to time series data in that it contains observations gathered regularly, chronologically. Below are some advantages of panel data:
- It can model both individual and common behaviors of groups
- It is more comprehensive, and has more efficiency and variability than cross-sectional data or time series data
- It can measure and determine the statistical effects that the standard cross-sectional or time series data can’t
- It can reduce estimation biases that may be caused by aggregate groups of data
Areas analyzed using panel data
Panel data analysis can be utilized in a wide variety of disciplines today. Below are some of the fields and data that can be analyzed using this technique:
- Unemployment across different states
- GDP across different countries
- International account balances
- Income dynamic studies
- World socioeconomic tables
- International trade tables
- Currency exchange rate tables
Epidemiology and health statistics
- Disease survival rate data
- Public health insurance data
- Child development and well-being
- Stock prices by firm
- Market volatility by firm or country
Difference between balanced data and unbalanced panel data analysis
Panel data analysis can be characterized as balanced or unbalanced. A balanced panel data has an equal number of observations across all groups being examined. Unbalanced panel data on the other hand has missing values, meaning, the number of observations is not the same for all the groups. It is important to note that some panel data models can only be used with balanced datasets. If you would like to have the differences of balanced and unbalanced panel data analysis expounded in depth, hire our professional panel data analysis online tutors.
Panel data and heterogeneity
Panel data analysis addresses the likely dependence of variables within the same group of data being observed. In fact, the biggest difference between a panel dataset and a time series data set is that the former allows for heterogeneity across the groups being observed and introduce individual-specific effects. Consider a panel data set that contains GDP data for five different countries; USA, Australia, Canada, France, and Greece:
- If there is a worldwide economic recession, the five countries are likely to be affected and changes will be caused in the GDP across all the five countries.
- If there is an election in Canada, the GDP of Canada is likely to be affected but it is unlikely that the GDP of the other countries will be affected.
- If there is a change in the South American trade policy, this change is only likely to affect a region of the United States and unlikely to affect the rest of the countries in the panel.
- If there is a change in the exchange rate of the Euro currency, the change will most directly affect only Greece and France.
Panel data analysis enables us to address the above heterogeneities effectively. Techniques like pure time series analysis and cross-sectional methods may not be applicable in the presence of heterogeneity.
Data scientists analyze sets of data with multiple observations over time to get the most desirable results. For instance, one may have a set of data covering the rate of production of numerous firms across several years. Analyzing and modeling such panels of data requires using methodologies specific to this kind of data. Panel data analysis provides just the perfect methodologies and models for manipulating this type of data. These include:
- Homogeneous panel data models: A homogenous data model assumes that the parameters in data are the same across individuals.
- Heterogeneous models: This model allows data parameters to vary or differ across individuals. Random effects and fixed effects (the two most important data manipulation techniques in panel data analysis) are good examples of heterogeneous panel data modeling. Other individual-specific panel data effects include:
- One way fixed effects
- Pooled ordinary least squares
- Random coefficients
- One way random effects
Panel data analysis is a complex topic in nature and students will sometimes require professional guidance. If you wish to have someone hold your hand through this topic, consider hiring our panel data analysis assignment help experts.