This Skill assists in generating LaTeX regression tables that conform to the formatting standards of **top economics journals (AER, QJE, Econometrica, JPE)**. It covers: standard three-line tables, panel data fixed effects tables, instrumental variable tables, and event study tables.
Applicable scenarios
- Need to insert standardized regression result tables in paper writing
- Convert Stata/Python/R output to LaTeX code
- Format three-line tables according to journal requirements
- Generate Main Table + Online Appendix Table
Core concepts
Core elements of top journal tables：
- Clear information hierarchy——Table header, groups, and variable names progress layer by layer
- Complete statistical information——Sample size, standard errors, clustering level, R², F statistic
- Standardized significance annotation——Unified stars (†///）
- Reproducible——Table code must correspond to regression code
Standard three-line table formatting specifications
Top journal table structure
┌─────────────────────────────────────────────┐
│ 表头（表标题 + 注释信息） │
├─────────────────────────────────────────────┤
│ 列标签（列1 列2 列3） │
│ ────────────────────────────────────────── │ ← 第一道线（顶部）
│ 变量行（因变量、自变量、控制变量） │
│ ────────────────────────────────────────── │ ← 第二道线（列分隔）
│ 统计量行（N、R²、F、聚类标准误） │
├─────────────────────────────────────────────┤
│ 表底注释（显著性标注、数据来源、稳健性说明） │
└─────────────────────────────────────────────┘
Significance star standards
| Symbol | p-value | Description |
|---|---|---|
| *** | p < 0.001 | 1% significance |
| ** | p < 0.01 | 5% significance |
| * | p < 0.05 | 10% significance |
| † | p < 0.10 | 15% significance (some journals) |
⚠️ Note: Different journals have different requirements for the number of stars and thresholds. Before submission, confirm the format of the target journal.
Common table LaTeX templates
Template 1: Standard OLS regression table
\begin{table}[htbp]
\centering
\caption{基准回归结果}
\label{tab:baseline}
\begin{threeparttable}
\begin{tabular}{l*{3}{c}}
\toprule
& \multicolumn{3}{c}{因变量: log(GDP per capita)} \\
\cmidrule(l){2-4}
& (1) & (2) & (3) \\
\midrule
互联网普及率 & 0.023*** & 0.018** & 0.015* \\
& (0.007) & (0.008) & (0.008) \\
控制变量 & 否 & 是 & 是 \\
固定效应 & 否 & 否 & 年份+国家\\
\midrule
观测值 & 1,240 & 1,240 & 1,240 \\
R² & 0.041 & 0.315 & 0.682 \\
\bottomrule
\end{tabular}
\begin{tablenotes}
\item \textit{注:} ***, **, * 分别表示1\%, 5\%, 10\%的显著性水平。括号内为聚类标准误（聚类在国家层面）。控制变量包括：教育年限、人口增长率、贸易开放度。
\end{tablenotes}
\end{threeparttable}
\end{table}
Template 2: Panel data fixed effects table
\begin{table}[htbp]
\centering
\caption{固定效应模型估计结果}
\label{tab:fe}
\begin{threeparttable}
\begin{tabular}{l*{4}{c}}
\toprule
& \multicolumn{2}{c}{OLS} & \multicolumn{2}{c}{固定效应} \\
\cmidrule(l){2-3} \cmidrule(l){4-5}
& (1) & (2) & (3) & (4) \\
\midrule
技术扩散指数 & 0.035*** & 0.028** & 0.021* & 0.018* \\
& (0.009) & (0.010) & (0.011) & (0.010) \\
\midrule
国家固定效应 & \checkmark & \checkmark & \checkmark & \checkmark \\
年份固定效应 & & \checkmark & & \checkmark \\
\midrule
观测值 & 1,240 & 1,240 & 1,240 & 1,240 \\
R² & 0.31 & 0.45 & 0.72 & 0.78 \\
\bottomrule
\end{tabular}
\begin{tablenotes}
\item \textit{注:} 同上。固定效应模型使用双向聚类标准误（国家+年份）。
\end{tablenotes}
\end{threeparttable}
\end{table}
Template 3: Instrumental variable method table
\begin{table}[htbp]
\centering
\caption{工具变量估计结果}
\label{tab:iv}
\begin{threeparttable}
\begin{tabular}{l*{3}{c}}
\toprule
& OLS & \multicolumn{2}{c}{2SLS} \\
\cmidrule(l){2-2} \cmidrule(l){3-4}
& (1) & (2) & (3) \\
\midrule
技术扩散指数 & 0.023*** & 0.041*** & 0.038*** \\
& (0.007) & (0.013) & (0.012) \\
\midrule
KP F统计量 & & 24.6 & 28.3 \\
弱工具变量检验 & & & \\
\midrule
观测值 & 1,240 & 1,240 & 1,240 \\
\bottomrule
\end{tabular}
\begin{tablenotes}
\item \textit{注:} 列(2)-(3)使用技术扩散的滞后值作为工具变量。KP F统计量>10通过弱工具变量检验。
\end{tablenotes}
\end{threeparttable}
\end{table}
Conversion tools from Stata/Python/R to LaTeX
Stata → LaTeX
// 安装 estout 套件
ssc install estout, replace
// 保存回归结果
eststo clear
eststo: reg ln_gdp internet i.year, vce(cluster country)
eststo: reg ln_gdp internet cov1 cov2 i.year, vce(cluster country)
// 导出 LaTeX
esttab using "tables/table1.tex", replace ///
title("基准回归结果") ///
label ///
booktabs ///
nonumbers ///
mtitles("OLS" "OLS") ///
star(* 0.05 ** 0.01 *** 0.001) ///
se ///
r2 ///
addn("控制变量包括教育年限、人口增长率、贸易开放度。")
Python (statsmodels) → LaTeX
import pandas as pd
from scipy.stats import ttest_ind
# 使用 statsmodels 输出的 LaTeX 转换
from statsmodels.iolib.summary import summary_table
# 回归后
result = model.fit()
print(result.summary_latex())
Three-line table formatting specifications (top journal requirements)
| Requirement | Description |
|---|---|
| Threeparttable | Use \begin{threeparttable} environment |
| booktabs | Use \toprule, \midrule, \bottomrule |
| Font | Usually 10pt; table notes can be 9pt |
| Column width | Use p{3cm} to control column width, or tabularx to adjust automatically |
| Number alignment | Numbers right-aligned, variable name column left-aligned |
| Missing values | Indicate missing values in the table with blank spaces, do not write "NA" |
Output specifications
## 表格输出规范
**输出路径**：tables/table{N}.tex
**主子表规范**：
- 主表（Main）：表格1-3，放入正文
- 附录表（Appendix）：表格A1-A10，放入Online Appendix
**文件名规范**：
tables/table1_baseline.tex # Baseline regression tables/table2_heterogeneity.tex # Heterogeneity analysis tables/tableA1_robustness_iv.tex # Appendix: IV robustness
**LaTeX 代码规范**：
- 表格必须可编译（无缺失 `}` 或 `{`）
- 所有特殊字符（%, &, #）需转义
Invocation interface
Enter in the Claude Code conversation window:
/latex-table
Or complete Prompt:
按LaTeX表格Skill生成符合AER顶刊格式的三线表回归结果，包括：基准回归表（表1）、固定效应表（表2）、工具变量表（表3）。使用booktabs环境，包含标准误聚类标注、显著性星标、固定效应标注。
Coordination with other Skills
did-reviewer→ DID regression results use the event study table formatR-optimizer→ Optimization when R outputs tablescodebook-pass→ Cleaned data is used directly for table generation
Important statement
- Tables must be reproducible——The regressions corresponding to the LaTeX table code must be re-runnable via Makefile
- Before submission, the star threshold and table size must be adjusted according to the target journal's format.
- Separation of main and subordinate tables—Place the core results in the main table, and put robustness and heterogeneity in the appendix.