Tables & Figures for RED (red-tables-figures)

When to trigger

• Building the exhibits that carry a quantitative dynamic paper
• Deciding how to show model-vs-data fit and the mechanism behind a result
• Making IRFs and policy experiments legible at print resolution

Exhibits that fit RED

RED papers live or die on whether the mechanism and the quantitative fit are visible. Favor:

• Impulse-response functions (IRFs) — the workhorse figure for dynamic models; plot model responses to the key shocks, overlay alternative parameterizations to isolate the mechanism, and (where relevant) overlay empirical responses.
• Calibration table — every parameter, value, source/target, and status (calibrated/estimated/assumed), so a reader can audit discipline at a glance.
• Moment-matching table — targeted and untargeted moments side by side, model vs data; untargeted fit is a credibility highlight, so give it prominence.
• Policy / counterfactual experiment figures — show the magnitude of the dynamic effect and the transition path, not just steady-state comparisons.
• Accuracy diagnostics — Euler-equation errors or convergence plots where the computation is non-trivial.

Make each exhibit self-contained: numbered, called out in order, with notes stating the model variant, shock, units, and data source. Author-year citations in notes match RED's reference system.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers. Full map: execution-with-mcp. RED is quantitative macro — mostly structural/calibration, which is outside this causal-inference toolchain; apply the chain to its empirical/reduced-form papers.

• Tables: etable (multi-model) or did_summary_to_latex straight from the result_id.
• Figures: plot_from_result / enhanced_event_study_plot / event_study_table — axis units and the SE/clustering note baked in.
• Every note names the estimator + clustering and states the magnitude in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Checklist

• IRFs / transition paths show the mechanism, not just outcomes
• Calibration and moment tables let a reader audit discipline and fit
• Untargeted moments are reported, not hidden
• Every exhibit is self-contained, numbered, and legible at print resolution

Anti-patterns

• Steady-state-only comparisons that hide the dynamics
• Moment tables showing only targeted moments
• Figures with no notes on model variant, shock, or units

Exhibit sequence

For most RED papers, the exhibit order should mirror the argument:

1. Mechanism figure: state variables or transition paths that show the dynamic force.
2. Discipline table: parameters, calibration targets, estimation targets, and sources.
3. Fit table: targeted and untargeted moments, model vs data.
4. Counterfactual/experiment: the quantitative result or policy path.
5. Accuracy/reproducibility note: solver error, convergence, seed, or runtime when computation matters.

If the first exhibit is a static regression table, ask whether the paper is really using RED's dynamic lens or drifting toward a field journal.

Mock moment table (model vs data)

The fit table referees scan first should look like this (all numbers illustrative):

Table X: Targeted and untargeted moments
                                     Data    Model   Source
Targeted
  Wealth-to-income ratio             2.90    2.90    NIPA / Flow of Funds
  Share with negative net worth      0.135   0.132   SCF
Untargeted
  Wealth Gini                        0.78    0.74    SCF
  Top-10% wealth share               0.71    0.66    SCF
  Average quarterly MPC              0.16    0.19    literature estimates (author-year)
Notes: model moments from the stationary distribution; simulation of 100,000
households; seed recorded in the archive readme.

The Targeted/Untargeted split must be typographically explicit — a single undifferentiated column invites the circularity objection from quantitative-macro referees.

Exhibit-budget anchor

Hedged anchor (verify against recent RED issues rather than treating it as policy): mainline quantitative papers commonly carry on the order of 4–8 main-text exhibits — typically one mechanism/IRF figure, a calibration table, a fit table, and one or two counterfactual exhibits — with accuracy diagnostics and extended sensitivity in an appendix. Treat that as a budget: each additional main-text exhibit must change what the reader believes about the mechanism or the magnitude.

Figure-craft pushback

Supplementary resources

• ../../resources/external_tools.md — plotting and solver outputs
• red-data-analysis — the analysis behind the exhibits