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

Content type	Output	Reference
Regression results (coefficients, SEs, R², N)	Stargazer-style coefficient table	references/table-generation.md
Summary statistics (means, SDs, quantiles)	Descriptive statistics panel	references/table-generation.md
Monte Carlo output (bias, RMSE, coverage)	Simulation results table	references/table-generation.md
Balance / covariate comparison	Balance table with normalized differences	references/table-generation.md
Transition probabilities	Matrix with row/column labels	references/table-generation.md
First-stage IV results	First-stage regression table	references/table-generation.md
Time-relative coefficients (leads/lags)	Event study plot	references/figure-generation.md
Running variable + cutoff	RD plot with local polynomial	references/figure-generation.md
Multiple estimates with CIs	Coefficient comparison plot	references/figure-generation.md
Sample sizes × effect sizes	Power curve	references/figure-generation.md
Group distributions	Density / kernel density plot	references/figure-generation.md
Two continuous variables	Binned scatter plot	references/figure-generation.md
Sorted estimates + indicator matrix	Specification curve	references/figure-generation.md

Format Defaults

Tables

Setting	Default
Format	LaTeX (booktabs: \toprule, \midrule, \bottomrule)
Stars	On coefficients, never on SEs (* p<0.10, ** p<0.05, *** p<0.01)
SEs	In parentheses, directly below coefficient
Decimal alignment	All numbers in a column align at decimal point
Fixed effects	Yes/No indicator rows, not coefficient rows
Negative numbers	Minus sign (economics convention), not parentheses
File location	tables/<descriptive-name>.tex
Label format	tab:<name>

Figures

Setting	Default
Font	Serif (Computer Modern / Times), 11-12pt labels
Size	6.5" × 4.5" (single column), 13" × 4.5" (two-panel)
DPI	Vector (PDF) primary, 300 DPI PNG secondary
Style	White background, no gridlines, bottom+left axes only
Color	Grayscale-friendly with distinct markers and line styles
Colorblind	Okabe-Ito or ColorBrewer Set2 when color is used
File location	figures/<descriptive-name>.pdf + .png
Label format	fig:<name>

Language-Specific Packages

Language	Tables	Figures
Python	pandas, stargazer, pystout, tabulate	matplotlib + seaborn
R	stargazer, modelsummary, kableExtra, gt, tinytable, fixest::etable()	ggplot2, coefplot, binsreg
Julia	PrettyTables.jl, Latexify.jl	Plots.jl, Makie.jl
Stata	esttab, outreg2, estout	twoway, coefplot, binscatter

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:

Pattern	Table panels	Figure layout
Multiple outcomes	Panel A/B/C by outcome	1×2 or 1×3 side-by-side
Multiple samples	Panel by subsample	2×1 stacked
Multiple methods	Panel by estimator (OLS/IV/GMM)	2×2 grid
Robustness variants	Columns within one panel	2×3 grid
Event study + pre-trends	—	2×1 stacked (estimates + test)

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