How to Do Panel Data Analysis in STATA
How to do Panel Data analysis in STATA
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Panel data is a set of data in which the behavior of variables or entities is observed over time. This type of data is also known as cross-sectional time-series data or longitudinal data. The variables contained in the data could be anything from companies and states to countries and individuals. Panel data allows researchers to control variables and entities that they cannot measure or observe such as the difference in business operations or cultural factors. To do this, they have to study and manipulate the panel data in a process called panel data analysis. STATA, one of the most popular tools for statistical analysis, has been used to perform panel data analysis for many years. Below, our STATA homework helpers explain how you can use this software to perform panel data analysis.
Panel data analysis methods used by STATA homework helpers
There are two techniques used for analyzing panel data in STATA. These are the same techniques that our panel data analysis assignment help experts use to complete projects issued from this area. They include:
As the name suggests, fixed effects are used only when analyzing the impact (effect) of entities or variables that vary over a given period of time. They study the correlation between the (independent) predictor and outcome variable within a given set of data. Each variable has specific attributes that may or may not affect the predictor variable. For example, the business operations of a given company may influence the price of its products or services. Or, belonging to a certain gender could influence the point of view toward certain issues.
One assumption we make when using fixed effects is that property within a given data set may influence or bias the outcome or independent variables. We, therefore, need to control this. Fixed effects remove the impacts of the time-invariant attributes, allowing us to examine the overall effect of the independent variables on the outcome variable.
Another assumption made when dealing with fixed effects models is that the time-invariant attributes are unique and should not have any relationship with other attributes of the data. Since these entities are different, each entity’s error term, as well as the constant, should not be related to other entities. If the error terms are related, the resulting inferences may be incorrect, hence fixed effects may not be the best method to analyze your current panel data. To further understand the concept of fixed effects, liaise with our STATA homework helpers.
Another technique used to analyze panel data in STATA is random effects. Unlike its fixed counterpart, the variation across different entities is considered random and unrelated to the independent variables contained in the data set. The upside of using random effects for panel data analysis is that time-invariant variables like gender can be included in the analysis. In the fixed-effects method, the time-invariant variables are consumed by the intercept.
If you believe that the various differences between entities can influence your dependent (response) variable, then you should apply the random effects technique. The technique assumes that the error term is not related to the predictors and this enables time-invariant variables to act as explanatory variables. For more information about random effects, connect with our STATA homework helpers.
Many students may have trouble analyzing panel data using STATA or dealing with projects that test their understanding of this concept. If you need professional assistance with this topic, feel free to take our panel data analysis assignment help.