Stata Panel Data -
To analyze panel data in Stata, you follow a structured workflow: preparing your data format, declaring the panel structure, and then running specific "xt" (cross-sectional time-series) commands. 1. Data Structure: Wide vs. Long Stata requires panel data to be in long format.
Wide Format: Each row is an entity, and time-varying variables are columns (e.g., gdp2010, gdp2011).
Long Format: Each row is an observation for a specific entity at a specific time point.
Command: If your data is wide, use the reshape command to convert it: reshape long gdp, i(country_id) j(year) Use code with caution. Copied to clipboard 2. Preparing Identifiers stata panel data
You need two identifier variables: a panel ID (entity) and a time ID (period).
Numeric requirement: The panel ID must be numeric. If your ID is a string (like country names), use encode to create a numeric version: encode country_name, gen(country_id) Use code with caution. Copied to clipboard
Group creation: If you lack a unique ID for groups, use egen: egen area_id = group(area_name) Use code with caution. Copied to clipboard 3. Declaring the Panel Structure To analyze panel data in Stata, you follow
Use the xtset command to tell Stata which variables define the panels and the time. xtset country_id year Use code with caution. Copied to clipboard
Stata will report if the panel is balanced (same number of time points for all entities) or unbalanced. 4. Core Panel Commands Once set, you can use specialized xt commands:
Intro 3 — Preparing data for analysis - Description - Stata Title: Leveraging Stata for Panel Data Analysis: A
Title:
Leveraging Stata for Panel Data Analysis: A Methodological Overview with Empirical Applications
Author: [Your Name]
Date: April 12, 2026
6. Discussion
The fixed effects model indicates that a 1 percentage point increase in FDI (% of GDP) raises GDP per capita by approximately 0.10% on average, holding other factors constant. Trade openness and capital formation also contribute positively. Diagnostic tests reveal heteroskedasticity, serial correlation, and cross-sectional dependence—common in macro panels. Hence, robust standard errors are essential for valid inference.
Marginal Effects after Nonlinear Models:
xtlogit emp wage hours, fe
margins, dydx(*) atmeans
Pros:
- Controls for all time-invariant confounders.
- Consistent under conditional independence.
5.2 Serial Correlation
xtserial gdp fdi trade gcf
Result: F(1,29) = 14.72, Prob > F = 0.001 → Serial correlation present.