Panel data analysis
Panel data or cross-sectional time-series data involves the collection of information on different variables under a specific timeframe. A spreadsheet with panel data contains columns such as the time, age, gender, and income. These forms of data are common these days and the major challenge is to analyze the panel data. Panel data analysis has raised a lot of curiosity from many data analysts because of its complexities. Here we will look at the general types of models applied to panel data.
In most cases, the modeling part is often challenging as compared to the other parts of data analysis. A suitable model will ensure the accuracy of the system. In case, you require instant help, contact our online panel data analysis assignment tutors.
There are two ways of conducting data analysis on panel data. The models commonly used are fixed effects and random effect models. Here you will learn the difference between the two and when it’s appropriate to use the two.
Fixed effects models commonly abbreviated to FE models are used whenever we need to analyze panel data containing variables that continuously change over time. These equations evaluate the relationship between a predictor and an entity such as a person or a government. The entities have certain characteristics that influence the predictor variable over time. In a fixed-effect model, we are interested to know the relationship, such as how do the political systems in a country influence the GDP? How does the gender of a person influence the opinion of a person towards a certain issue?
As can be seen, FE models assume that there is an internal characteristic in the entity that can influence the predictor variable. These time-invariant characteristics are at the core of FE models and are unique and have different error terms for each entity.
What you should note about the fixed effects models is that they study the change caused by those time-invariant characteristics, and they do not study the cause of the time-invariant characteristics.
Random effects model
The rationale of the random effects is that changes amongst all entities in the data are taken to be random and uncorrelated. The random effect models do not limit you to using the time-variant entities. These time-variant entities are included in the intercept of the model. They also assume that entity errors are uncorrelated with the predictor variables. This means the time-invariant variable can be included as the explanatory variables.
Under what circumstances is the random effect model suitable for your analysis of panel data? Use the random effects models if you have reasons to believe that the variation among all the entities has an effect on your exploratory variable.
Selecting between the two
There is a statistical test that can give you an accurate result on which of the two models is better suited for your data. The Hausman test in Stata. This is a hypothesis test containing the null and the alternative hypothesis. It states that the best model for the panel data is the fixed effect model while for the former the best model to use is the random effect model. It runs a test to check whether the errors are correlated or not.
The first requirement before running the test is to carry out both models and save the estimates for each, and finally, you run the test. The best model is selected depending on the magnitude of the p-values.
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