Stata Panel Data Exclusive -

Panel data (longitudinal data) combines cross-sectional units observed over time. Stata’s xt suite provides a dedicated, efficient workflow. This text covers all essential steps without extraneous filler.

  • Test for serial correlation:
  • Provide marginal effects and predicted values where policy relevance requires concrete numbers.

    • When dealing with large panels (large N) where cross-sectional dependence is suspected (e.g., global financial crises affecting all countries), standard clustering is insufficient. Stata offers xtscc (user-written) or manual implementation of Driscoll-Kraay standard errors.

    * ssc install xtscc
xtscc y x1 x2, fe

    This produces standard errors that are robust to heteroskedasticity, serial correlation, and cross-sectional dependence simultaneously. stata panel data exclusive

    "Panel Data Models in Stata"

  • Stata commands: xtreg, xtset, xttest2, xtserial, xtsum.

    • A panel requires two identifiers: a cross-sectional unit (id) and a time variable (time). Data can be wide (one row per unit, time in columns) or long (one row per unit-time pair). Stata requires long form. Test for serial correlation:

    Convert wide to long:

    reshape long y x, i(id) j(year)

    Declare panel:

    xtset id year

    Output shows: balanced/unbalanced, delta, min/max time periods.

    Check:

    xtdescribe      // pattern, gaps, frequency
xtsum           // within/between variation summary
tsreport, list  // identify gaps if unbalanced

    Key insight: Strong within-unit variation (over time) vs. between-unit variation determines model choice.

    "Panel Data Models for Binary and Count Outcomes" Provide marginal effects and predicted values where policy

  • Exclusive trick: Use xtlogit, fe for conditional fixed-effects logit — only within-panel variation identified.
    • Oben