Format Regression Table

Create a publication-quality regression table from estimation output in a notebook.

Arguments

• $ARGUMENTS — a notebook reference and/or description of the table (e.g., "notebook-02 OLS results" or "main regression table with 3 specifications")

Steps

1. Identify the source notebook and the estimation output:

   • If a notebook name is provided, read that notebook
   • If no notebook is specified, ask the user which notebook contains the regression results
   • Look for cells with estimation commands (Python: statsmodels, linearmodels; R: lm, fixest, felm; Stata: reg, reghdfe, ivregress)

2. Ask the user for table specifications:

   • Which models/columns to include
   • Dependent variable name(s)
   • Which coefficients to display (or "all")
   • Fixed effects to report as Yes/No rows
   • Clustering level for standard errors
   • Any custom notes for the table footer

3. Construct the table following academic conventions:

   • Header row: Dependent variable name spanning all columns, column numbers (1), (2), (3)...
   • Coefficient rows: Point estimate on top, standard error in parentheses below
   • Significance stars: * p<0.10, ** p<0.05, *** p<0.01
   • Fixed effects rows: Yes/No indicators
   • Summary rows: Observations (N), R-squared, Adjusted R-squared, or other fit statistics
   • Footer: Significance legend and notes about standard errors

4. Create or update a cell in the specified notebook with:

   • Cell directive: #| label: tbl-<descriptive-name> (or *| for Stata)
   • Cell directive: #| tbl-cap: "<caption>" (or *| for Stata)
   • The code to generate the formatted Markdown table
   • Stata caveat: Do NOT use tbl- prefix for Stata text output — use a plain label instead (e.g., stata-regression)

5. Sync the Jupytext pair:

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

6. Show the user the embed shortcode to paste into index.qmd:

   {{< embed notebooks/<name>.ipynb#tbl-<label> >}}
   

Error handling

• If the notebook has no estimation output, report this and ask the user to run the regressions first.
• If the estimation output format is not recognized, ask the user to provide the raw coefficients and standard errors.