Panel data is typically preferred in , where each row represents a unique combination of entity and time, rather than wide format , where multiple observations for one entity exist in a single row.
* 1. Run and save the Fixed Effects model xtreg income education age, fe estimates store fe_model * 2. Run and save the Random Effects model xtreg income education age, re estimates store re_model * 3. Run the Hausman test hausman fe_model re_model Use code with caution. Interpreting the Results If
Standard procedure in Stata:
This decomposition is fundamental: it tells you whether most of the variation in a variable is across units (e.g., race, gender) or within units over time (e.g., income, unemployment status). stata panel data
If the assumptions of FE/RE are violated, advanced methods are required.
In Stata, panel data is represented as a dataset with two or more dimensions: a cross-sectional dimension (e.g., individuals, firms) and a time series dimension (e.g., years, quarters). Each observation represents a single unit at a specific point in time. Stata's panel data capabilities allow users to exploit the advantages of panel data, including:
Many outcomes are not continuous. Stata provides: Panel data is typically preferred in , where
Choosing the correct model requires statistical testing rather than visual guessing. Pooled OLS vs. Fixed Effects (F-Test)
Panel data often suffers from heteroskedasticity and autocorrelation. Testing for Autocorrelation Use the Wooldridge test for serial correlation: xtserial y x1 x2 Use code with caution. Testing for Heteroskedasticity Use the modified Wald test after xtreg, fe : xttest3 Use code with caution. Robust Standard Errors
To convert your data from wide to long, use the reshape command: reshape long income expense, i(id) j(year) Use code with caution. Run and save the Random Effects model xtreg
This provides a summary of the panel structure, including the number of panels, time periods, and whether gaps exist.
: The xtline command creates a separate line graph for every entity in your dataset, which is great for spotting outliers . xtline gdp 3. Choosing the Right Model