table

name	table
description	Econometrics skill for creating publication-quality LaTeX regression and summary tables. Activates when the user asks about: "regression table", "LaTeX table", "esttab", "stargazer", "modelsummary", "publication table", "format results", "multi-panel table", "journal table", "export regression results", "table formatting", "回归表格", "LaTeX表格", "结果导出", "论文表格", "回归结果格式化", "多模型表格"

LaTeX Table Formatting Skill

This skill generates publication-quality regression tables, summary statistics tables, and multi-panel layouts for economics journals. Covers the major table-making tools: esttab/estout (Stata), modelsummary/fixest::etable (R), and stargazer (R/Python).

Quick Decision: Which Tool to Use

Tool	Language	Best For
esttab/estout	Stata	Most flexible; Stata-native workflows
modelsummary	R	Modern, clean API; many output formats
fixest::etable	R	Fast tables from fixest regressions
stargazer	R	Classic; widely used in econ
statsmodels summary + manual	Python	Custom formatting

Regression Tables

Stata — esttab/estout

* Stata — multi-model regression table
ssc install estout

* Run models
eststo clear
eststo m1: reg y x1, robust
eststo m2: reg y x1 x2, robust
eststo m3: reg y x1 x2 x3, robust
eststo m4: reghdfe y x1 x2 x3, absorb(fe_var) cluster(cluster_var)

* Export to LaTeX
esttab m1 m2 m3 m4 using "results.tex", replace ///
    b(3) se(3) ///                          // 3 decimal places
    star(* 0.10 ** 0.05 *** 0.01) ///       // significance stars
    title("Main Results") ///
    mtitles("OLS" "OLS" "OLS" "FE") ///     // column titles
    label ///                                // use variable labels
    keep(x1 x2 x3) ///                      // show only key vars
    order(x1 x2 x3) ///
    stats(N r2 r2_a, fmt(%9.0fc %9.3f %9.3f) ///
          labels("Observations" "R-squared" "Adj. R-squared")) ///
    addnotes("Robust standard errors in parentheses." ///
             "*** p<0.01, ** p<0.05, * p<0.1") ///
    booktabs ///                             // professional formatting
    fragment                                 // no \begin{table} wrapper

* Multi-panel table
esttab m1 m2 using "panel_a.tex", replace booktabs fragment ///
    prehead("\begin{table}[htbp]" "\centering" "\caption{Results}" ///
            "\begin{tabular}{lcc}" "\toprule" ///
            "& \multicolumn{2}{c}{\textit{Panel A: Full Sample}} \\" ///
            "\cmidrule(lr){2-3}")
esttab m3 m4 using "panel_b.tex", replace booktabs fragment ///
    prehead("\midrule" ///
            "& \multicolumn{2}{c}{\textit{Panel B: Subsample}} \\" ///
            "\cmidrule(lr){2-3}") ///
    postfoot("\bottomrule" "\end{tabular}" ///
             "\begin{tablenotes}" "\small" ///
             "\item Standard errors in parentheses." ///
             "\end{tablenotes}" "\end{table}")

R — modelsummary

# R — modelsummary (modern, flexible)
library(modelsummary)

m1 <- lm(y ~ x1, data = df)
m2 <- lm(y ~ x1 + x2, data = df)
m3 <- lm(y ~ x1 + x2 + x3, data = df)

# LaTeX output
modelsummary(
  list("(1)" = m1, "(2)" = m2, "(3)" = m3),
  coef_map = c("x1" = "Treatment",
               "x2" = "Control 1",
               "x3" = "Control 2"),
  gof_map = c(  
  stars          
  title  
  notes  
  output      



modelsummary
    m1   m2   m3
  add_rows  tribble
    term            
               
            
  
  output  

R — fixest::etable

# R — etable (fast, built into fixest)
library(fixest)

m1 <- feols(y ~ x1, data = df, vcov = "HC1")
m2 <- feols(y ~ x1 + x2 | year, data = df, vcov = ~cluster_var)
m3 <- feols(y ~ x1 + x2 | year + industry, data = df, vcov = ~cluster_var)

etable(m1, m2, m3,
       tex = TRUE,
       file = "results.tex",
       dict = c(x1 = "Treatment", x2 = "Control"),
       order =  
       drop  
       fixef.group    
                            
       style.tex  style.tex    
       title  
       notes  

R — stargazer

# R — stargazer (classic)
library(stargazer)

stargazer(m1, m2, m3,
          type = "latex",
          out = "results.tex",
          title = "Main Results",
          dep.var.labels = "Outcome Variable",
          covariate.labels = c("Treatment", "Control 1", "Control 2"),
          keep = c("x1", "x2", "x3"),
          add.lines = list(
            c("Year FE", "No", "Yes", "Yes"),
            c("Industry FE"   
          
          omit.stat   
          notes  
          notes.align  
          star.cutoffs    

Python — Manual LaTeX Generation

# Python — generate LaTeX table from statsmodels
import statsmodels.formula.api as smf

models = {
    '(1)': smf.ols('y ~ x1', data=df).fit(cov_type='HC1'),
    '(2)': smf.ols('y ~ x1 + x2', data=df).fit(cov_type='HC1'),
    '(3)': smf.ols('y ~ x1 + x2 + x3', data=df).fit(cov_type='HC1'),
}

# Using statsmodels summary_col
from statsmodels.iolib.summary2 import summary_col
result = summary_col(list(models.values()),
                     stars=True,
                     float_format='%.3f',
                     model_names=list(models.keys()),
                     info_dict={'N': lambda x: f"{int(x.nobs)}",
                                'R²': lambda x: f"{x.rsquared:.3f}"})
print(result.as_latex())

# For more control, use pystout:
# pip install pystout
from pystout import pystout
pystout(models=list(models.values()),
        file='results.tex',
        endog_names=list(models.keys()),
        exognames=['x1', 'x2', 'x3'],
        stars={0.1: '*', 0.05: '**', 0.01: '***'})

Journal-Specific Styles

AER (American Economic Review)

# fixest style
etable(m1, m2, m3, style.tex = style.tex("aer"), tex = TRUE)

Key conventions: booktabs rules, no vertical lines, significance noted in footnote not with stars (AER discourages stars).

QJE / ReStud / Econometrica

* Stata — clean academic style
esttab m1 m2 m3 using "results.tex", replace ///
    b(3) se(3) star(* 0.10 ** 0.05 *** 0.01) ///
    booktabs fragment ///
    alignment(D{.}{.}{-1}) ///
    prehead("\begin{table}[htbp]" "\centering" ///
            "\caption{Title Here}\label{tab:main}" ///
            "\begin{tabular}{l*{3}{D{.}{.}{-1}}}" "\toprule") ///
    postfoot("\bottomrule" "\end{tabular}" ///
             "\begin{tablenotes}[flushleft]\footnotesize" ///
             "\item \textit{Notes:} Standard errors in parentheses." ///
             " *** p$<$0.01, ** p$<$0.05, * p$<$0.1" ///
             "\end{tablenotes}" "\end{table}")

Multi-Panel and Complex Layouts

Side-by-Side Panels

* Panel A: OLS, Panel B: IV
esttab m_ols1 m_ols2 using "table.tex", replace booktabs fragment ///
    prehead("\begin{table}[htbp]\centering" ///
            "\caption{OLS and IV Estimates}" ///
            "\begin{tabular}{lcc}\toprule" ///
            "& \multicolumn{2}{c}{\textit{Panel A: OLS}} \\" ///
            "\cmidrule(lr){2-3}")
esttab m_iv1 m_iv2 using "table.tex", append booktabs fragment ///
    prehead("\midrule" ///
            "& \multicolumn{2}{c}{\textit{Panel B: IV/2SLS}} \\" ///
            "\cmidrule(lr){2-3}") ///
    postfoot("\bottomrule\end{tabular}\end{table}")

Interaction Effects Table

# R — interaction table
library(modelsummary)
m_interaction <- lm(y ~ x1 * group, data = df)
modelsummary(m_interaction,
             coef_rename = c("x1" = "Treatment",
                             "group" = "Group",
                             "x1:group" = "Treatment × Group"),
             output = "interaction.tex")

Tips for Clean Tables

Tip	Details
Use booktabs	\toprule, \midrule, \bottomrule instead of \hline
No vertical lines	Standard in economics journals
Align decimals	Use dcolumn package with D{.}{.}{-1} column type
Stars in notes	Clearly state significance levels in table notes
Variable labels	Use descriptive names, not variable codes
Fixed effects rows	Show Yes/No indicators for FE inclusions
Consistent decimals	3 decimals for coefficients/SE; 0 for N
Notes placement	Below the table, left-aligned, smaller font

Common Pitfalls

• Too many decimals: 3 is standard for coefficients; more is noise
• Missing clustering info: Always state what SE are clustered on
• Forgetting FE indicators: Reviewers need to know which FE are included
• Stars without notes: Always define significance levels
• Cramming too many models: 4–6 columns is typical maximum

LaTeX Integration: Paper-Ready Output

When the output will be \input{}-ed into a compiled paper (rather than compiled standalone), three things consistently cause failures. Address them upfront.

1. Body-Only Files — No Document Wrapper

Tools like esttab, stargazer, and manual Python scripts often emit a standalone .tex file with \documentclass...\begin{document}...\end{document}. This breaks \input{} in the parent paper because LaTeX cannot nest document environments.

Always generate two versions: the full standalone file for spot-checking, and a body-only file stripped of the document wrapper for inclusion in the paper.

# Python — strip wrapper and save body-only file
import re

def save_body_only(tex_path):
    """Strip \documentclass...\\end{document} wrapper; keep only the table content."""
    with open(tex_path) as f:
        txt = f.read()
    m = re.search(r'\\begin\{document\}(.*?)\\end\{document\}', txt, re.DOTALL)
    body = m.group(1).strip() if m else txt
    body_path = tex_path.replace('.tex', '_body.tex')
    with open(body_path, 'w') as f:
        f.write(body)
    return body_path

In the parent paper, include as:

\input{tables/table2_main_results_body}   % no .tex extension needed

Make sure each body file contains the full \begin{table}...\end{table} block — not just the \begin{tabular} fragment. A missing \begin{table} wrapper causes \multicolumn and \caption errors at compile time.

2. Avoid siunitx by Default

The siunitx package (used for the S decimal-aligned column type) is absent in many TeX distributions and causes ! LaTeX Error: File 'siunitx.sty' not found. Prefer standard column types:

% Instead of: \begin{tabular}{l S S S}   (requires siunitx)
% Use:        \begin{tabular}{l c c c}    (always works)

% For strict decimal alignment without siunitx, use the dcolumn package:
\usepackage{dcolumn}                       % ships with every standard TeX distro
\begin{tabular}{l D{.}{.}{-1} D{.}{.}{-1}}

For most robustness and heterogeneity tables, c columns are sufficient — the numbers are clearly readable without strict decimal alignment.

Also avoid Unicode characters in Python-generated .tex files. Characters like >=, ->, <= typed directly will break LaTeX. Always use their LaTeX equivalents: $\geq$, $\rightarrow$, $\leq$.

3. Overflow Prevention for Wide Tables

A table with 6 or more columns, or with a text description column, will almost certainly overflow the page width in portrait mode. Apply these fixes together:

% Rule: >=6 columns → wrap in landscape; text description column → use p{Xcm} not l

\usepackage{pdflscape}    % add to preamble

% In the body file:
\begin{landscape}
\begin{table}[ht]
\centering
\caption{...}
\begin{threeparttable}
{\footnotesize\setlength{\tabcolsep}{4pt}     % shrink font + column padding
\begin{tabular}{p{4.5cm} c c c c c c}         % p{} for text col, c for data cols
...
\end{tabular}}
\begin{tablenotes}[flushleft]\small
\item \textit{Notes}: ...
\end{tablenotes}
\end{threeparttable}
\end{table}
\end{landscape}

Quick reference for portrait mode (1.25in margins, ~16.5cm text width):

Columns	First column	Approach
3-4	l	Portrait, no special treatment needed
5-6	p{4.5cm} + {\footnotesize\setlength{\tabcolsep}{4pt}}	Portrait, tight
7+	p{Xcm} + \footnotesize	Landscape always

Keep \begin{tablenotes} text concise — a long inline math expression that cannot line-break (e.g., a full regression formula) will produce an Overfull \hbox even when the table itself fits. Summarize the spec in plain language in the note and put the equation in the methods section instead.

Related Skills & Commands

• stats: Summary statistics tables (Table 1)
• ols-regression: Generate regression results to format
• /robustness: Side-by-side robustness specifications tables
• /method: Methods section references the tables
• paper-writing: Tables are a key component of the paper