Generate Robustness Checks and Table

Generate Robustness Checks and Table

Given a baseline regression, generate code for standard robustness checks and format the results as a publication-ready table.

Arguments

• $ARGUMENTS — notebook reference and baseline specification description (e.g., "notebook-02 baseline OLS with GDP on life expectancy")

Steps

1. Read the specified notebook and locate the baseline regression:

   • Look for estimation commands (Python: statsmodels, linearmodels; R: lm, fixest, felm; Stata: reg, reghdfe, ivregress)
   • Identify the dependent variable, independent variables, fixed effects, and clustering

2. Ask the user which robustness checks to include:

   • Alternative control variable sets (drop/add controls)
   • Alternative fixed effects specifications
   • Different standard error clustering levels
   • Subsample analysis (e.g., by region, time period, income group)
   • Winsorized or trimmed dependent variable
   • Alternative dependent variable (e.g., log vs level)
   • Placebo tests (randomized treatment, pre-period outcome)
   • Alternative estimation methods (e.g., OLS vs Poisson, logit vs probit)

3. Generate code cells in the notebook for each robustness specification:

   • Each cell should be self-contained (loads data, runs regression, stores results)
   • Use consistent variable naming for results collection

4. Create a summary cell that collects all results into a single table:

   • Cell directive: #| label: tbl-robustness (or *| for Stata)
   • Cell directive: #| tbl-cap: "Robustness checks" (or *| for Stata)
   • Format: baseline in column (1), each robustness check in subsequent columns
   • Follow academic conventions: coefficient (SE), significance stars, N, R², FE indicators
   • Stata caveat: Do NOT use tbl- prefix for Stata text output — use a plain label (e.g., stata-robustness)

5. Optionally export the table to tables/ as a standalone file (LaTeX or CSV)

6. Sync the Jupytext pair:

   uv run jupytext --sync notebooks/<name>.md