Run panel data regression in Stata

 Here’s a detailed Medium-style blog post tailored for students, researchers, or professionals looking to run a panel regression in Stata.

📊 How to Run a Panel Regression in Stata: A Step-by-Step Guide

Panel data (also known as longitudinal data) tracks the same units over time — making it incredibly powerful for analyzing changes across time while controlling for individual-specific effects. In this post, I’ll walk you through how to run a panel regression in Stata, from data setup to interpretation.

🧠 What is a Panel Regression?

A panel regression estimates relationships in data that has both cross-sectional (across individuals) and time-series (across time) dimensions. This allows for richer analysis than traditional cross-sectional or time-series alone.

The general form:

y_{it} = \alpha + \beta X_{it} + u_{i} + \epsilon_{it}

Where:

• i indexes individuals (e.g., countries, firms)
• t indexes time
• u_i captures unobserved individual-specific effects
• X_{it} are the independent variables
• \epsilon_{it} is the idiosyncratic error

🔧 Step 1: Preparing Your Data

Your dataset must be long-form, with each observation representing a unit-time pair.

Example structure:

id	year	y	x1	x2
1	2010	5.2	3.1	1.2
1	2011	5.4	3.0	1.4
2	2010	6.2	2.9	1.0
…	…	…	…	…

Each panel must have a unique identifier (id) and a time variable (year).

⚙️ Step 2: Declare the Panel Structure

Use the xtset command:

xtset id year

This tells Stata you’re working with panel data. You should see something like:

Panel variable: id (unbalanced)

Time variable: year, 2010 to 2020

If your panel is balanced (each unit has the same time periods), it’ll note that.

📈 Step 3: Run a Panel Regression

1. 

Fixed Effects Model

 (within estimator)

Controls for time-invariant heterogeneity:

xtreg y x1 x2, fe

Stata will drop any time-invariant variables automatically in this mode.

2. 

Random Effects Model

Assumes individual effects are random and uncorrelated with the regressors:

xtreg y x1 x2, re

🧪 Step 4: Choosing Between Fixed and Random Effects

Run the Hausman test to decide between FE and RE:

xtreg y x1 x2, fe

estimates store fixed

xtreg y x1 x2, re

estimates store random

hausman fixed random

If the Hausman test is significant (p < 0.05), go with Fixed Effects. If not, Random Effects is acceptable.

🧼 Optional: Add Robust Standard Errors

To control for heteroskedasticity or autocorrelation:

xtreg y x1 x2, fe vce(robust)

📉 Interpreting the Output

Example output:

------------------------------------------------------------------------------

         y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

         x1 |   0.4532     0.1023     4.43   0.000     0.2523     0.6541

         x2 |  -0.2311     0.0877    -2.63   0.010    -0.4027    -0.0595

------------------------------------------------------------------------------

Interpretation:

• A 1-unit increase in x1 is associated with a 0.45 increase in y, holding other factors constant.
• x2 has a negative and significant impact.

🪛 Bonus: Time and Individual Fixed Effects

Want to control for both unit and time-specific effects?

xtreg y x1 x2 i.year, fe

i.year includes year dummies.

🚀 Wrapping Up

Running panel regressions in Stata is straightforward but powerful. Remember:

1. Use xtset to declare panel structure.
2. Choose between fe and re using economic logic and Hausman test.
3. Use robust standard errors to improve inference.
4. Interpret coefficients with the model context in mind.

💬 Got Questions?

If you’re stuck or want to go further (e.g., dynamic panels, GMM), leave a comment — or follow for future tutorials!

Would you like me to generate a thumbnail image or share a code template notebook to go with this post?