table

name	table
description	Called by /plot to upgrade regression and summary tables to top-journal standards.

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: pyfixest (Python, primary), esttab/estout (Stata), modelsummary/fixest::etable (R), and stargazer (R/Python).

Step 0: Workflow Interface

When called by /plot or another upstream command, this skill accepts a structured context block. Do not re-ask the user for information already present in the context.

table_type:       table1_descriptive | table2_balance | table3_main | table_robustness | table_heterogeneity
strategy:         DiD | RDD | IV | PanelFE | SC | OLS
model_results:    path to regression output .csv (e.g., tables/table3_main_results.csv)
software:         python | r | stata
cluster_var:      variable name or "none"
fe_list:          e.g., ["unit FE", "time FE"] or []
sample_desc:      e.g., "Firm-year panel, 2000–2015, N=12,450"
project_name:     string

When called standalone (not via /plot), prompt the user for table_type, strategy, and software before proceeding.

Software → tool routing (execute only the matching section; skip all others):

software	Primary tool	Section to execute
python	pyfixest.etable()	Python — pyfixest.etable()
r	modelsummary + fixest::etable	R — modelsummary / R — fixest::etable
stata	esttab/estout	Stata — esttab/estout

Routing rules:

1. Generate code only for the software specified in the caller context. Do not generate parallel versions in other languages unless the user explicitly requests them.
2. For software = r: if the upstream /code command used fixest::feols, prefer fixest::etable() (native, zero conversion); use modelsummary when the user needs .docx or .html output in addition to .tex.
3. For software = python: pyfixest objects (feols, fepois, iv2sls) pass directly to etable(). Do not use statsmodels or pystout unless pyfixest is unavailable.
4. For software = stata: reghdfe → eststo → esttab is the standard chain. Always call ssc install estout check at the top of the do-file.

Output convention (always dual-format):

Every table must be saved in two files:

• tables/tableN_name.tex — body-only file for \input{} in the paper
• tables/tableN_name.csv — raw numbers for manual verification

Table type → template routing:

table_type	Template	Key Features
table1_descriptive	Descriptive stats	Variable groups, mean/sd/min/max/N, \multicolumn section headers
table2_balance	Balance test	Pre-treatment filter enforced, normalized diff column, ✅/⚠️ flag
table3_main	Main regression	Multi-column, FE rows, controls Yes/No, clustered SE note
table_robustness	Robustness	Panel layout (Panel A/B/C), same dep. var across specs
table_heterogeneity	Heterogeneity	Subgroup headers, interaction terms, p-value of difference

Quick Decision: Which Tool to Use

Tool	Language	Best For
pyfixest.etable()	Python ⚡	Primary tool; works with feols/fepois/iv2sls from /code
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

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

* --- CSV dual-output (always run alongside LaTeX export) ---
* Export coefficient table as CSV for manual verification
esttab m1 m2 m3 m4 using "tables/table3_main_results.csv", replace ///
    b(3) se(3) star(* 0.10 ** 0.05 *** 0.01) ///
    csv plain noobs

* 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

Workflow note: modelsummary accepts fixest::feols objects directly — no conversion needed. If /code used feols, pass those objects straight into modelsummary() or fixest::etable(). Prefer fixest::etable() for speed; use modelsummary() when you need .docx or .html output alongside .tex.

# 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
  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 — pyfixest.etable() (Primary)

pyfixest is the standard Python estimation tool used by the /code command. Its etable() function produces booktabs-ready LaTeX and a tidy DataFrame simultaneously, enabling dual-format output with no extra work.

# Python — pyfixest regression table (primary workflow)
import pyfixest as pf
import pandas as pd
from pathlib import Path

TABLES_DIR = Path("tables")
TABLES_DIR.mkdir(exist_ok=True)

# Run models (mirrors /code output)
m1 = pf.feols("y ~ x1", data=df, vcov="HC3")
m2 = pf.feols("y ~ x1 + x2 | unit_id", data=df, vcov={"CRV1": "unit_id"})
m3 = pf.feols("y ~ x1 + x2 | unit_id + year", data=df, vcov={"CRV1": "unit_id"})
m4 = pf.feols("y ~ x1 + x2 | unit_id + year", data=df,
               vcov={"CRV1": "unit_id^year"})  # double-clustered

# --- LaTeX output (.tex) ---
etable_tex = pf.etable(
    [m1, m2, m3, m4],
    digits=3,
    stars=True,                        # *** 0.01, ** 0.05, * 0.10
    coef_fmt="b (se)",                 # coefficient with SE below
    dict={                             # rename variables
        "x1": "Treatment",
        "x2": "Log Income",
        "Intercept": "",               # hide intercept label
    },
    keep=["Treatment", "Log Income"],  # show only key vars
    drop_intercept=True,
    tex=True,
    # Fixed effects and controls indicators added manually below
)


fe_block = 


 ():
    
    
    body = etable_str.replace(, fe_block + )
     (
         + 
         + 
         + 
         + 
        + body + 
         + 
         + 
         + 
        
    )

notes_str = (
    
    
    
    
)

full_tex = build_table_tex(
    etable_tex, fe_block,
    caption=,
    label=,
    notes=notes_str
)

tex_path = TABLES_DIR / 
tex_path.write_text(full_tex)
()


coef_df = pf.etable([m1, m2, m3, m4], digits=, =)
csv_path = TABLES_DIR / 
coef_df.to_csv(csv_path, index=)
()

FE indicator pattern: Always add fixed-effects and controls indicator rows manually after etable() — pyfixest does not auto-generate these rows. The fe_block pattern above is the standard template.

IV / 2SLS tables with pyfixest:

# First stage + Second stage in one table
first  = pf.feols("d ~ z + x1 | unit_id + year", data=df, vcov={"CRV1": "unit_id"})
second = pf.feols("y ~ 1 | unit_id + year | d ~ z", data=df, vcov={"CRV1": "unit_id"})
reduced = pf.feols("y ~ z + x1 | unit_id + year", data=df, vcov={"CRV1": "unit_id"})

iv_tex = pf.etable([first, second, reduced], digits=3,
                   dict={"d": "Treatment (D)", "z": "Instrument (Z)",
                         "fit_d": "Treatment (D) — IV"},
                   tex=True)
# Add first-stage F-stat row manually:
fstat = first.F_stat()  # pyfixest attribute
iv_tex = iv_tex.replace(
    r"\bottomrule",
    rf"First-stage F & {fstat:.1f} & & \\" + "\n" + r"\bottomrule"
)

## Journal-Specific Styles

### AER (American Economic Review)

```r
# 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

Standardized Notes Format

Every table must include a Notes line below the bottom rule. Notes are not optional — journals reject tables without them. Use this template, filling in the bracketed fields:

Notes: [SE type] in parentheses[, clustered at the [unit] level]. [FE description.]
[Sample description: unit, time period, N.] [Any variable transformation or
winsorization.] *** p<0.01, ** p<0.05, * p<0.1.

Strategy-specific Notes templates:

Strategy	Notes Template
DiD / TWFE	"Standard errors double-clustered by unit and year in parentheses. Regressions include unit and year fixed effects. Sample: [panel, years, N]."
Event Study	"Standard errors clustered at the [unit] level. Reference period: t = −1. Coefficients plot relative effects; pre-period joint test p-value = [p]."
RDD	"Bias-corrected robust standard errors (rdrobust) in parentheses. Bandwidth h = [h] selected by MSE-optimal selector. Local linear polynomial."
IV / 2SLS	"Two-stage least squares. Standard errors clustered at the [unit] level. First-stage F-statistic = [F] (Stock-Yogo weak instrument critical value 10% = 16.38)."
Panel FE	"Standard errors clustered at the [unit] level. Hausman test p-value = [p] (favors FE over RE). Regressions include [FE list]."
OLS	"Heteroskedasticity-robust standard errors (HC3) in parentheses."

LaTeX Notes implementation:

% Recommended: threeparttable + tablenotes (renders correctly in all TeX distributions)
\usepackage{threeparttable}

\begin{table}[htbp]
\centering
\caption{...}
\begin{threeparttable}
\begin{tabular}{...}
...
\end{tabular}
\begin{tablenotes}[flushleft]
\footnotesize
\item \textit{Notes:} Standard errors double-clustered by firm and year in
parentheses. Regressions include firm and year fixed effects. Sample covers
manufacturing firms, 2000--2015 ($N = 12{,}450$ firm-year observations).
Revenue is winsorized at the 1st and 99th percentiles.
*** $p<0.01$, ** $p<0.05$, * $p<0.1$.
\end{tablenotes}
\end{threeparttable}
\end{table}

Avoid: bare \footnote{} for table notes (misplaces the note); tabular note rows (breaks alignment); notes that only say "Standard errors in parentheses" with no clustering or FE information.

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
• No CSV dual-output: Always save .csv alongside .tex for verification

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.