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regression-table

Formats estimation output as a publication-quality regression table with stars, SEs, and fit statistics. Use when creating a results table.

Published by brycewang-stanford ↗brycewang-stanford/Auto-Empirical-Research-Skills

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regression-table

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Use regression-table to format estimation output in [notebook] as a publication-quality regression table using [table specifications], sync its Jupytext pair, and provide the manuscript embed shortcode.

Required inputs

  • [notebook]
  • [table specifications]

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SKILL.md

name: regression-tabledescription: Formats estimation output as a publication-quality regression table with stars, SEs, and fit statistics. Use when creating a results table.argument-hint: <notebook> [description]allowed-tools: Bash, Read, Write, Edit, Glob, Grep

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.