Publication Output

Publication Output

Generate publication-quality tables and figures for academic research papers. Routes to the appropriate output type based on content, applies standard academic formatting conventions, and produces files ready for LaTeX inclusion.

When to Use

Skip when:

• The task is choosing an empirical method or running estimation (use empirical-playbook or causal-inference skill)
• The task is journal submission logistics or referee responses (use submission-guide skill)
• Results are exploratory and not yet ready for formatted output (finish estimation first)

Use when:

• After estimation: format regression results, diagnostics, or robustness checks into tables
• After simulation: format Monte Carlo results (bias, RMSE, coverage) into comparison tables
• For descriptive work: summary statistics, balance tables, transition matrices
• For visualization: event studies, RD plots, coefficient plots, power curves, densities, specification curves

Output Type Router

Format Defaults

Tables

Figures

Language-Specific Packages

Package notes:

• pystout (Python) — estout-style regression tables for statsmodels and linearmodels (OLS, IV2SLS, PanelOLS). Supports mgroups for column grouping, modstat for custom statistics rows.
• tinytable (R) — lightweight, native Typst support, used as modelsummary backend.
• fixest::etable() (R) — direct from estimation, handles multi-way FE notation automatically.

Automated vs semi-automated tradeoff: Automated tools (esttab, stargazer) are quick but hard to customize. Semi-automated tools (save intermediates, generate LaTeX separately) are harder to start but easier to customize. Costs are convex for automated, concave for semi-automated.

Quarto+Typst: Quarto with Typst backend offers sub-second compilation for iterative work. Use keep-tex: true for journal submission when you need the raw LaTeX output.

Multi-Panel Assembly

Tables and figures often require multi-panel layouts:

Ensure consistent axis scales, font sizes, and formatting across panels. Label panels as (a), (b), (c) or Panel A, Panel B, Panel C.

Quick Examples

Python: Regression Table

import pandas as pd
from stargazer.stargazer import Stargazer
from linearmodels.iv import IV2SLS

# Format results with stargazer
stargazer = Stargazer([ols_result, iv_result])
stargazer.custom_columns(["OLS", "IV/2SLS"])
stargazer.show_model_numbers(False)
stargazer.significant_digits(3)
with open("tables/main-results.tex", "w") as f:
    f.write(stargazer.render_latex())

Python: Event Study Plot

import matplotlib.pyplot as plt
import matplotlib

matplotlib.rcParams.update({"font.family": "serif", "font.size": 11})
fig, ax = plt.subplots(figsize=(6.5, 4.5))
ax.errorbar(leads_lags, coefficients, yerr=1.96 * se, fmt="o-", color="black", capsize=3)
ax.axhline(y=0, color="gray", linestyle="--", linewidth=0.8)
ax.axvline(x=-0.5, color="red", linestyle=":", linewidth=0.8)
ax.set_xlabel("Periods relative to treatment")
ax.set_ylabel("Coefficient estimate")
ax.spines[["top", "right"]].set_visible(False)
fig.savefig("figures/event-study.pdf", bbox_inches="tight")

Quality Checklist

Before finalizing any output:

• Decimal alignment consistent within each column
• Stars attached to coefficients only (never to SEs)
• SE format consistent throughout (parentheses or brackets, not mixed)
• Sample sizes sum correctly across panels
• Column/axis labels clear and unambiguous
• Significance note present if stars used
• LaTeX compiles without errors
• Figures readable in grayscale (B&W print)
• No default titles on figures (titles go in \caption)
• All information encoded in color also encoded in shape/line style

Integration with Other Components

• econometric-reviewer agent preloads this skill to audit tables against code output
• econometric-reviewer may request formatted output during /workflows:review
• /workflows:compound captures table/figure templates into docs/solutions/
• Companion outputs: regression table → summary statistics table; event study → pre-trend test figure