Stata Panel Data Instant

reshape wide stubname, i(panelvar) j(timevar)

If a variable has zero within variation, it is time-invariant (e.g., race, country of origin). This heavily impacts your modeling choices. 3. Core Panel Data Models

Choosing between Pooled OLS, Fixed Effects, and Random Effects should not be arbitrary. Stata provides formal statistical tests to guide your selection. Pooled OLS vs. Random Effects (Breusch-Pagan LM Test)

The standard summarize command aggregates all observations together. To decompose your data variation, use xtsum : xtsum GDP inflation Use code with caution. Calculated across the entire dataset ( stata panel data

xtivreg y (x1 = z1 z2) x2, fe

* Difference GMM xtabond y x1 x2, gmm(y) iv(x1 x2) * System GMM (via user-written xtabond2) ssc install xtabond2 xtabond2 y l.y x1 x2, gmm(l.y) iv(x1 x2) nolevel small Use code with caution. Panel Unit Root Tests For long panels (where time

The basic syntax is:

Each row represents a unique combination of an individual and a specific time period. Stata requires the long format for panel data commands.

Each row represents an individual, with repeated measures appearing in separate columns (e.g., income2021 , income2022 ).

Model selection xttest0 hausman fe re

The Fixed Effects model controls for all time-invariant, unobserved characteristics of your entities (e.g., cultural factors, innate ability, geographic location). It only examines variation within an entity over time. xtreg y x1 x2, fe Use code with caution. Random Effects (RE)

Some units have missing time periods. (Stata handles unbalanced panels automatically for most commands). 2. Exploring and Describing Panel Data

Panel data frequently suffer from two statistical problems: and heteroskedasticity . Stata provides several ways to detect and correct them. reshape wide stubname, i(panelvar) j(timevar) If a variable

Panel datasets often suffer from complications like heteroskedasticity, serial correlation, and cross-sectional dependence. Testing for Heteroskedasticity