67-econfin-workflow-toolkit-figure

brycewang-stanford/Auto-Empirical-Research-SkillsData VisualizationAudit pendingSnapshot 31299223fcde

Summary

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SKILL.md

Publication-Quality Figures Skill

This skill generates figure code that meets the formatting standards of top economics journals (AER, QJE, ReStud, Econometrica, JPE). It covers the most common econometric figure types with precise control over fonts, colors, dimensions, and export formats.

Journal Requirements Summary

Requirement	AER / QJE / ReStud Standard
File format	PDF (vector) or EPS; PNG at 300+ DPI for raster
Dimensions	Width: 3.4in (single column) or 7in (full page); height ≤ 9in
Font	Matching journal body font; minimum 8pt for labels
Colors	Must be readable in grayscale; avoid red-green pairs
Line width	≥ 0.5pt for data lines; ≥ 0.75pt for axes
Legend	Inside plot area or below; no box border preferred
Notes	Figure notes below, starting with "Notes:"
Numbering	"Figure 1:", "Figure 2:" etc. in caption

Base Setup: Journal-Ready Defaults

Python (matplotlib)

# Python — journal-quality defaults
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np

# AER/QJE style defaults
plt.rcParams.update({
    'figure.figsize': (7, 4.5),
    'figure.dpi': 300,
    'font.family': 'serif',
    'font.serif': ['Times New Roman', 'Computer Modern Roman'],
    'font.size': 11,
    'axes.labelsize': 12,
    'axes.titlesize': 13,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'axes.linewidth': 0.8,
    'lines.linewidth': 1.5,
    'lines.markersize': 5,
    'axes.spines.top': False,
    'axes.spines.right': False,
    'savefig.bbox': 'tight',
    'savefig.pad_inches': 0.05,
})

# Grayscale-safe color palette
COLORS = ['#2c3e50', '#e74c3c', '#3498db', '#27ae60', '#f39c12', '#8e44ad']
MARKERS = ['o', 's', '^', 'D', 'v', 'P']
LINESTYLES = ['-', '--', '-.', ':', (0, (3, 1, 1, 1)), (0, (5, 2))]

# R — journal-quality ggplot2 theme
library(ggplot2)
library(scales)

theme_econ <- function(base_size = 11, base_family = "serif") {
  theme_minimal(base_size = base_size, base_family = base_family) %+replace%
    theme(
      # Clean axes
      panel.grid.major = element_line(color = "grey90", linewidth = 0.3),
      panel.grid.minor = element_blank(),
      axis.line = element_line(color = "black", linewidth = 0.5),
      axis.ticks = element_line(color = "black", linewidth = 0.3),
      # Text
      axis.title = element_text(size = rel(1.1)),
      axis.text = element_text(size = rel(0.9), color = "black"),
      plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
      plot.subtitle = element_text(size = rel(0.95), color = "grey30"),
      plot.caption = element_text(size = rel(0.8), hjust = 0, color = "grey40"),
      # Legend
      legend.position = "bottom",
      legend.title = element_blank(),
      legend.background = element_blank(),
      legend.key = element_blank(),
      # Margins
      plot.margin = margin(10, 15, 10, 10)
    )
}

# Grayscale-safe palette
econ_colors <- c("#2c3e50", "#e74c3c", "#3498db", "#27ae60", "#f39c12", "#8e44ad")

# Export function
save_econ_fig <- function(plot, filename, width = 7, height = 4.5) {
  ggsave(filename, plot, width = width, height = height, dpi = 300,
         device = cairo_pdf)  # vector PDF with embedded fonts
}

* Stata — journal-quality graph scheme
set scheme s2color
graph set window fontface "Times New Roman"

* Global graph options for consistency
global graph_opts ///
    graphregion(color(white) margin(small)) ///
    plotregion(margin(medium)) ///
    ylabel(, angle(horizontal) nogrid labsize(small)) ///
    xlabel(, labsize(small)) ///
    legend(region(lcolor(none)) size(small) rows(1) position(6))

* Export as PDF
graph export "figure.pdf", as(pdf) replace

# Python — event study plot
import pandas as pd

def plot_event_study(coefs, ses, periods, ref_period=-1,
                     treatment_time=0, title="Event Study",
                     ylabel="Coefficient Estimate"):
    fig, ax = plt.subplots(figsize=(7, 4.5))

    # Confidence intervals
    ci_lower = [c - 1.96 * s for c, s in zip(coefs, ses)]
    ci_upper = [c + 1.96 * s for c, s in zip(coefs, ses)]

    # Plot CI band
    ax.fill_between(periods, ci_lower, ci_upper,
                    alpha=0.15, color=COLORS[0])
    # Point estimates
    ax.plot(periods, coefs, 'o-', color=COLORS[0],
            markersize=5, linewidth=1.5, zorder=5)

    # Reference lines
    ax.axhline(y=0, color='black', linewidth=0.5, linestyle='-')
    ax.axvline(x=treatment_time - 0.5, color='grey', linewidth=0.8,
               linestyle='--', alpha=0.7)

    # Labels
    ax.set_xlabel("Periods Relative to Treatment")
    ax.set_ylabel(ylabel)
    ax.set_title(title)
    ax.annotate("Treatment", xy=(treatment_time - 0.5, ax.get_ylim()[1] * 0.9),
                fontsize=9, color='grey40', ha='right')

    plt.tight_layout()
    plt.savefig("event_study.pdf")
    return fig

# R — event study with fixest
library(fixest)

es <- feols(y ~ i(rel_time, treat, ref = -1) | entity_id + year,
            data = df, cluster = ~entity_id)

# fixest iplot (quick)
iplot(es, xlab = "Periods Relative to Treatment",
      ylab = "Coefficient Estimate",
      main = "Event Study: Dynamic Treatment Effects")

# ggplot2 version (full control)
library(broom)
es_df <- tidy(es, conf.int = TRUE) %>%
  filter(grepl("rel_time", term)) %>%
  mutate(period = as.numeric(gsub(".*::(-?\\d+):.*", "\\1", term)))

# Add reference period
es_df <- bind_rows(es_df,
  tibble(period = -1, estimate = 0, conf.low = 0, conf.high = 0))

ggplot(es_df, aes(x = period, y = estimate)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high),
              fill = econ_colors[1], alpha = 0.15) +
  geom_point(color = econ_colors[1], size = 2) +
  geom_line(color = econ_colors[1], linewidth = 0.8) +
  geom_hline(yintercept = 0, linewidth = 0.5) +
  geom_vline(xintercept = -0.5, linetype = "dashed", color = "grey50") +
  labs(x = "Periods Relative to Treatment",
       y = "Coefficient Estimate",
       title = "Event Study: Dynamic Treatment Effects",
       caption = "Notes: 95% confidence intervals shown. Standard errors clustered at entity level.") +
  theme_econ()

save_econ_fig(last_plot(), "event_study.pdf")

* Stata — event study plot
reghdfe y ib(-1).rel_time, absorb(entity_id year) cluster(entity_id)

coefplot, vertical drop(_cons) ///
    yline(0, lcolor(black) lwidth(thin)) ///
    xline(4.5, lcolor(gs8) lpattern(dash)) ///
    ciopts(recast(rcap) lcolor(navy) lwidth(thin)) ///
    mcolor(navy) msymbol(circle) ///
    ytitle("Coefficient Estimate") ///
    xtitle("Periods Relative to Treatment") ///
    title("Event Study: Dynamic Treatment Effects") ///
    note("Notes: 95% CIs shown. SEs clustered at entity level.") ///
    $graph_opts
graph export "event_study.pdf", as(pdf) replace

# Python — coefficient plot comparing specifications
def plot_coefplot(models, model_names, var_names, var_labels=None):
    fig, ax = plt.subplots(figsize=(7, 0.6 * len(var_names) + 1.5))
    var_labels = var_labels or var_names
    n_models = len(models)
    offsets = np.linspace(-0.15 * (n_models-1), 0.15 * (n_models-1), n_models)

    for j, (coefs, ses, name) in enumerate(zip(
        [m['coefs'] for m in models],
        [m['ses'] for m in models],
        model_names)):

        y_pos = np.arange(len(var_names)) + offsets[j]
        ax.errorbar(coefs, y_pos, xerr=1.96 * np.array(ses),
                    fmt='o', color=COLORS[j], markersize=5,
                    capsize=3, linewidth=1.2, label=name)

    ax.axvline(x=0, color='black', linewidth=0.5)
    ax.set_yticks(range(len(var_names)))
    ax.set_yticklabels(var_labels)
    ax.set_xlabel("Coefficient Estimate")
    ax.legend(loc='lower right', frameon=False)
    ax.invert_yaxis()
    plt.tight_layout()
    plt.savefig("coefplot.pdf")

# R — coefficient plot with modelsummary/modelplot
library(modelsummary)

modelplot(list("OLS" = m1, "IV" = m2, "FE" = m3),
          coef_map = c("x1" = "Treatment", "x2" = "Income", "x3" = "Education"),
          color = "model") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  labs(x = "Coefficient Estimate", y = "") +
  scale_color_manual(values = econ_colors[1:3]) +
  theme_econ()

# Python — binned scatter with linear fit
def plot_binscatter(x, y, n_bins=20, controls=None, xlabel="X", ylabel="Y",
                    title="", residualize=False):
    import statsmodels.api as sm

    if residualize and controls is not None:
        # Residualize both x and y on controls
        x = sm.OLS(x, sm.add_constant(controls)).fit().resid
        y = sm.OLS(y, sm.add_constant(controls)).fit().resid

    # Create bins
    bins = pd.qcut(x, n_bins, duplicates='drop')
    bin_means = pd.DataFrame({'x': x, 'y': y, 'bin': bins}).groupby('bin').mean()

    fig, ax = plt.subplots(figsize=(7, 4.5))
    ax.scatter(bin_means['x'], bin_means['y'], color=COLORS[0],
               s=40, zorder=5, edgecolors='white', linewidth=0.5)

    # Linear fit
    z = np.polyfit(bin_means['x'], bin_means['y'], 1)
    x_line = np.linspace(bin_means['x'].min(), bin_means['x'].max(), 100)
    ax.plot(x_line, np.polyval(z, x_line), color=COLORS[1],
            linewidth=1.2, linestyle='--')

    slope, pval = z[0], sm.OLS(y, sm.add_constant(x)).fit().pvalues[1]
    ax.annotate(f"Slope = {slope:.3f} (p = {pval:.3f})",
                xy=(0.05, 0.95), xycoords='axes fraction',
                fontsize=10, va='top')

    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    ax.set_title(title)
    plt.tight_layout()
    plt.savefig("binscatter.pdf")

# R — binscatter with binsreg (Cattaneo et al.)
library(binsreg)
binsreg(y = df$y, x = df$x, w = ~ x1 + x2,  # controls
        data = df, dots = c(0, 0), line = c(3, 3),
        ci = c(3, 3), cb = c(3, 3),
        title = "Binned Scatter: Y vs X",
        x.label = "X Variable", y.label = "Y Variable")

# ggplot2 manual version
library(dplyr)
df_binned <- df %>%
  mutate(bin = ntile(x, 20)) %>%
  group_by(bin) %>%
  summarize(x = mean(x), y = mean(y))

ggplot(df_binned, aes(x = x, y = y)) +
  geom_point(color = econ_colors[1], size = 3) +
  geom_smooth(method = "lm", se = FALSE, color = econ_colors[2],
              linetype = "dashed", linewidth = 0.8) +
  labs(x = "X Variable", y = "Y Variable") +
  theme_econ()

* Stata — binscatter
ssc install binscatter

binscatter y x, controls(x1 x2) nquantiles(20) ///
    lcolor(navy) mcolor(navy) ///
    ytitle("Y Variable") xtitle("X Variable") ///
    title("Binned Scatter: Y vs X") ///
    $graph_opts
graph export "binscatter.pdf", as(pdf) replace

# Python — RDD plot with local polynomial fit
def plot_rdd(running_var, outcome, cutoff, bandwidth=None,
             n_bins=20, title="Regression Discontinuity"):
    fig, ax = plt.subplots(figsize=(7, 4.5))

    # Binned means (separate for each side)
    for side, mask, color in [("Control", running_var < cutoff, COLORS[0]),
                               ("Treated", running_var >= cutoff, COLORS[1])]:
        x_side = running_var[mask]
        y_side = outcome[mask]
        bins = pd.qcut(x_side, min(n_bins, len(x_side)//5), duplicates='drop')
        bm = pd.DataFrame({'x': x_side, 'y': y_side, 'bin': bins}).groupby('bin').mean()
        ax.scatter(bm['x'], bm['y'], color=color, s=35, zorder=5,
                   edgecolors='white', linewidth=0.5)

        # Local polynomial fit
        z = np.polyfit(x_side, y_side, 1)
        x_fit = np.linspace(x_side.min(), x_side.max(), 200)
        ax.plot(x_fit, np.polyval(z, x_fit), color=color, linewidth=1.5)

    # Cutoff line
    ax.axvline(x=cutoff, color='grey', linewidth=1, linestyle='--')
    ax.set_xlabel("Running Variable")
    ax.set_ylabel("Outcome")
    ax.set_title(title)

    if bandwidth:
        ax.axvspan(cutoff - bandwidth, cutoff + bandwidth,
                   alpha=0.05, color='grey')

    plt.tight_layout()
    plt.savefig("rdd_plot.pdf")

# R — RDD plot (rdrobust)
library(rdrobust)
rdplot(y = df$y, x = df$running_var, c = cutoff,
       title = "Regression Discontinuity",
       x.label = "Running Variable", y.label = "Outcome",
       col.dots = econ_colors[1:2], col.lines = econ_colors[1:2])

# ggplot2 version
ggplot(df, aes(x = running_var, y = y)) +
  geom_point(aes(color = running_var >= cutoff), alpha = 0.15, size = 1) +
  geom_smooth(data = filter(df, running_var < cutoff),
              method = "lm", formula = y ~ poly(x, 1),
              color = econ_colors[1], fill = econ_colors[1], alpha = 0.1) +
  geom_smooth(data = filter(df, running_var >= cutoff),
              method = "lm", formula = y ~ poly(x, 1),
              color = econ_colors[2], fill = econ_colors[2], alpha = 0.1) +
  geom_vline(xintercept = cutoff, linetype = "dashed", color = "grey40") +
  scale_color_manual(values = econ_colors[1:2], guide = "none") +
  labs(x = "Running Variable", y = "Outcome",
       title = "Regression Discontinuity Design") +
  theme_econ()

* Stata — RDD plot
rdplot y running_var, c(cutoff) ///
    graph_options(title("Regression Discontinuity") ///
    ytitle("Outcome") xtitle("Running Variable") ///
    $graph_opts)
graph export "rdd_plot.pdf", as(pdf) replace

# Python — overlapping density plot
from scipy.stats import gaussian_kde

def plot_density(groups, labels, xlabel="Value", title=""):
    fig, ax = plt.subplots(figsize=(7, 4.5))
    for i, (data, label) in enumerate(zip(groups, labels)):
        kde = gaussian_kde(data, bw_method='silverman')
        x_grid = np.linspace(data.min() - data.std(), data.max() + data.std(), 300)
        ax.plot(x_grid, kde(x_grid), color=COLORS[i], linewidth=1.5, label=label)
        ax.fill_between(x_grid, kde(x_grid), alpha=0.1, color=COLORS[i])
    ax.set_xlabel(xlabel)
    ax.set_ylabel("Density")
    ax.set_title(title)
    ax.legend(frameon=False)
    plt.tight_layout()
    plt.savefig("density.pdf")

# R — density comparison
ggplot(df, aes(x = outcome, fill = group, color = group)) +
  geom_density(alpha = 0.15, linewidth = 0.8) +
  scale_fill_manual(values = econ_colors[1:2]) +
  scale_color_manual(values = econ_colors[1:2]) +
  labs(x = "Outcome", y = "Density",
       title = "Distribution by Group") +
  theme_econ()

* Stata — density comparison
twoway (kdensity outcome if group == 0, lcolor(navy) lwidth(medthick)) ///
       (kdensity outcome if group == 1, lcolor(cranberry) lwidth(medthick) ///
        lpattern(dash)), ///
       legend(label(1 "Control") label(2 "Treatment")) ///
       ytitle("Density") xtitle("Outcome") ///
       title("Distribution by Group") ///
       $graph_opts
graph export "density.pdf", as(pdf) replace

# Python — time series with shaded recession bars
def plot_timeseries(dates, series_dict, recessions=None,
                    ylabel="", title=""):
    fig, ax = plt.subplots(figsize=(7, 4.5))
    for i, (label, values) in enumerate(series_dict.items()):
        ax.plot(dates, values, color=COLORS[i], linewidth=1.5,
                linestyle=LINESTYLES[i], label=label)

    if recessions:
        for start, end in recessions:
            ax.axvspan(start, end, alpha=0.08, color='grey')

    ax.set_ylabel(ylabel)
    ax.set_title(title)
    ax.legend(frameon=False, loc='best')
    fig.autofmt_xdate()
    plt.tight_layout()
    plt.savefig("timeseries.pdf")

# R — time series with recession shading
library(ggplot2)

ggplot(df, aes(x = date, y = value)) +
  geom_rect(data = recessions,
            aes(xmin = start, xmax = end, ymin = -Inf, ymax = Inf),
            inherit.aes = FALSE, fill = "grey", alpha = 0.1) +
  geom_line(aes(color = series, linetype = series), linewidth = 0.8) +
  scale_color_manual(values = econ_colors) +
  labs(x = "", y = "Value", title = "Time Series Comparison") +
  theme_econ()

# Python — treated vs synthetic control + gap
def plot_synth(years, treated, synthetic, treatment_year, title=""):
    fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))

    # Panel A: Levels
    ax = axes[0]
    ax.plot(years, treated, color=COLORS[0], linewidth=1.5, label="Treated")
    ax.plot(years, synthetic, color=COLORS[1], linewidth=1.5,
            linestyle='--', label="Synthetic Control")
    ax.axvline(x=treatment_year, color='grey', linewidth=0.8, linestyle='--')
    ax.set_title("(a) Treated vs. Synthetic Control")
    ax.set_ylabel("Outcome")
    ax.legend(frameon=False)

    # Panel B: Gap
    ax = axes[1]
    gap = np.array(treated) - np.array(synthetic)
    ax.plot(years, gap, color=COLORS[0], linewidth=1.5)
    ax.fill_between(years, 0, gap, where=np.array(years) >= treatment_year,
                    alpha=0.15, color=COLORS[0])
    ax.axhline(y=0, color='black', linewidth=0.5)
    ax.axvline(x=treatment_year, color='grey', linewidth=0.8, linestyle='--')
    ax.set_title("(b) Treatment Effect (Gap)")
    ax.set_ylabel("Treated − Synthetic")

    for ax in axes:
        ax.set_xlabel("Year")
    plt.tight_layout()
    plt.savefig("synth_plot.pdf")

# Python — multi-panel layout
fig, axes = plt.subplots(2, 2, figsize=(12, 9))

for i, (ax, data, title) in enumerate(zip(
    axes.flat, datasets, panel_titles)):
    ax.scatter(data['x'], data['y'], color=COLORS[0], s=20, alpha=0.5)
    ax.set_title(f"({chr(97+i)}) {title}", fontsize=11)
    ax.set_xlabel("X")
    ax.set_ylabel("Y")

plt.tight_layout()
plt.savefig("multipanel.pdf")

# R — multi-panel with patchwork
library(patchwork)

p1 <- ggplot(df, aes(x, y1)) + geom_point(size = 1, alpha = 0.3) +
  labs(title = "(a) Panel A") + theme_econ()
p2 <- ggplot(df, aes(x, y2)) + geom_point(size = 1, alpha = 0.3) +
  labs(title = "(b) Panel B") + theme_econ()
p3 <- ggplot(df, aes(x, y3)) + geom_line() +
  labs(title = "(c) Panel C") + theme_econ()
p4 <- ggplot(df, aes(x, y4)) + geom_line() +
  labs(title = "(d) Panel D") + theme_econ()

combined <- (p1 | p2) / (p3 | p4)
save_econ_fig(combined, "multipanel.pdf", width = 12, height = 9)

* Stata — multi-panel with graph combine
graph combine panel_a panel_b panel_c panel_d, ///
    rows(2) cols(2) ///
    title("Figure 1: Main Results") ///
    $graph_opts
graph export "multipanel.pdf", as(pdf) replace

figures/
  fig01_aaei_distribution.pdf
  fig02_parallel_trends.pdf
  fig03_event_study_wages.pdf
  fig04_event_study_employment.pdf
  fig05_heterogeneity_wage_group.pdf
  ...

import os, matplotlib.pyplot as plt

FIGURES_DIR = "figures"
os.makedirs(FIGURES_DIR, exist_ok=True)

def save_fig(fig, fig_num: int, name: str, formats=("pdf",)):
    """Save to figures/figNN_name.ext with consistent naming."""
    for fmt in formats:
        path = os.path.join(FIGURES_DIR, f"fig{fig_num:02d}_{name}.{fmt}")
        fig.savefig(path, bbox_inches='tight', dpi=300)
        print(f"Saved: {path}")
    return path

# Usage:
fig, ax = plt.subplots(figsize=(7, 4.5))
# ... plotting code ...
save_fig(fig, fig_num=3, name="event_study_wages")

FIGURES_DIR <- "figures"
dir.create(FIGURES_DIR, showWarnings = FALSE, recursive = TRUE)

save_fig <- function(plot, fig_num, name, width = 7, height = 4.5) {
  path <- file.path(FIGURES_DIR, sprintf("fig%02d_%s.pdf", fig_num, name))
  ggsave(path, plot, width = width, height = height, device = cairo_pdf)
  cat("Saved:", path, "\n")
  invisible(path)
}

# Usage:
p <- ggplot(...) + theme_econ()
save_fig(p, fig_num = 3, name = "event_study_wages")

% In preamble — point to figures/ once:
\graphicspath{{../figures/}}

% In the paper body — no path needed in each call:
\begin{figure}[htbp]
  \centering
  \includegraphics[width=0.9\textwidth]{fig03_event_study_wages}
  \caption{Event Study: Effect of AI Exposure on Log Wages}
  \label{fig:event_study}
\end{figure}

67-econfin-workflow-toolkit-figure

Summary

SKILL.md

Publication-Quality Figures Skill

Journal Requirements Summary

Base Setup: Journal-Ready Defaults

Python (matplotlib)

R (ggplot2)

Stata

Common Econometric Figure Types

1. Event Study / Dynamic Treatment Effects

2. Coefficient Plot (Multiple Models)

3. Binned Scatter Plot (binscatter)

4. RDD Visualization

5. Kernel Density / Distribution Comparison

6. Time Series / Trend Plot

7. Synthetic Control Gap Plot

8. Multi-Panel Figures

Formatting Checklist

Common Pitfalls

Output File Management

Standard Convention

Python Helper

R Helper

In the LaTeX Paper

Related Skills & Commands

Related skills