This report outlines the essential steps, commands, and best practices for conducting panel data analysis in Stata. Panel data (or longitudinal data) tracks multiple entities over several time periods, allowing researchers to control for unobserved individual heterogeneity. 1. Data Preparation and Setup
Before running regressions, you must format your data so Stata recognizes its panel structure.
Long Format: Stata generally requires data in "long" format, where each row represents one observation per entity per time period.
If your data is in "wide" format (e.g., years as columns), use the command: reshape long [variable_stub], i(id) j(year).
Declaring the Panel: Use the xtset command to define the individual identifier ( ) and the time variable ( Command: xtset id_variable time_variable.
Stata will report if the panel is "strongly balanced" (no missing years for any entity) or "unbalanced". 2. Core Estimation Models
Panel analysis typically involves choosing between three main linear models: Panel Data Analysis Fixed and Random Effects using Stata stata panel data
In Stata, panel data (also known as longitudinal data) consists of observations of the same entities—such as individuals, firms, or countries—over multiple time periods
. To effectively analyze and report on this data, you must first structure it correctly and then use specialized "xt" commands. Princeton University 1. Data Structure and Preparation Stata requires panel data to be in long format
, where each row represents a single entity at a single point in time.
: If your data is in "wide" format (one row per entity with multiple columns for different years), use the reshape long Declaration : You must tell Stata the data is a panel using the xtset panelvar timevar xtset country year 2. Descriptive Reporting
Before running regressions, use these commands to report the structure and balance of your panel: Panel Data Analysis Fixed and Random Effects using Stata
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. This report outlines the essential steps, commands, and
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
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 you have attrition bias.
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
xtreg, fe)FE is Stata’s superstar. It controls for time-invariant unobservables (e.g., corporate culture, country geography). But:
xtreg, fe reports an F-test that all u_i=0. If rejected, FE > pooled OLS. But no one mentions: FE’s standard errors are biased without clustering.Cool trick: Run xtreg, fe vce(cluster id) as default. Always. Even if you think errors are i.i.d.—they aren’t.
Before any analysis, Stata must know which variable identifies the panel (individual) and which identifies time.
use "http://www.stata-press.com/data/r18/nlswork.dta", clear
xtset idcode year
Output interpretation: Stata reports balanced/unbalanced status and time deltas. Use xtdes to describe the panel structure and xtsum to summarize within and between variation.
xtdescribe, patterns
Shows which periods are missing for which panels. If missingness correlates with outcomes, you have attrition bias.