/aejmac-robustness

name: aejmac-robustness
description: Use when the headline result of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript must be shown stable across specification, sample, identification, and tuning choices. Builds the robustness program a macro referee will demand; it does not establish the primary identification or model (use aejmac-identification / aejmac-theory-model first).

Robustness Program (aejmac-robustness)

When to trigger

• The headline number rests on one specification, one sample, one lag length, or one grid
• A referee could ask "is this an artifact of [choice]?" and you have no panel of alternatives
• The empirical IRF and the model-implied response are compared but only at the baseline
• A structural/calibrated result has never been re-run under alternative targets

The AEJ: Macro robustness bar

Macro inference is fragile in characteristic ways: **short effective samples**, **structural breaks** (Great Moderation, ZLB, COVID), **specification forks** (lag length, detrending, prior, calibration target), and **method dependence** (SVAR vs. LP; perturbation vs. global). The AEJ: Macro robustness bar is to show the **headline quantity survives the choices a skeptical macro referee would flip**, and to be honest where it does not. Robustness is not a graveyard of extra tables — it is a targeted defense of the specific number the paper claims.

A macro robustness program (build the panel)

Empirical (SVAR / LP / narrative)

• **Sample splits**: pre/post-1984 (Great Moderation), exclude/keep the ZLB period, exclude COVID; report whether the response is stable.
• **Specification**: lag length, detrending/filtering choice (HP vs. one-sided vs. none), control set, levels vs. differences.
• **Method cross-check**: if SVAR is baseline, corroborate with LP (and vice versa); agreement is strong evidence.
• **Inference**: alternative HAC bandwidths / clustering; weak-instrument-robust bands for proxy-VAR/LP-IV.
• **Identification variants**: alternative orderings / sign sets / instrument constructions.

Quantitative (DSGE / HANK / structural)

• **Alternative calibration targets** and parameter ranges; show how the headline quantity moves.
• **Alternative solution method / accuracy** (higher perturbation order, finer grid) where nonlinearity matters.
• **Alternative model elements** (Taylor-rule coefficients, adjustment costs, market structure) the referee will name.
• **Estimation**: alternative moments / priors; re-estimate on a subsample.

Cross-cutting

• **External validity**: another country / dataset / period where the mechanism should also hold.
• **Placebo / falsification**: a response that should be zero (pre-shock leads; a non-targeted series).

Reporting discipline

• Lead with a **one-paragraph summary** of what is robust and what is not, then a compact robustness table/figure.
• Keep the **baseline number visible** in every robustness exhibit so the reader sees the movement.
• Put the bulk in the **online appendix**; main text carries the decisive checks only.
• A spec-curve / multiverse plot is powerful for empirical macro when many forks exist.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). AEJ: Macro mixes empirical and structural work — local projections (`local_projections` / `irf`) are in StatsPAI, but DSGE / calibration estimation is outside this causal-inference toolchain.

• **Many outcomes / specifications:** `romano_wolf` (step-down FWER, accounts for

cross-test correlation) or `benjamini_hochberg` — report the adjusted threshold.

• **OVB sensitivity:** `oster_delta` / `sensemakr` — the confounder strength that would

overturn the headline.

• **Inference:** `wild_cluster_bootstrap` (few clusters), `twoway_cluster` / `conley`.
• **Re-fit off one handle:** `audit_result(result_id)` lists the missing checks and the

exact `suggest_function` for each — no guessing the battery.

• **Exhibits:** `etable` / `did_summary_to_latex` from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive (now actually-run) battery in the appendix. See the executed chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

Checklist

• [ ] The specific choices a referee would flip are enumerated
• [ ] Sample splits across the relevant macro breaks (Great Moderation / ZLB / COVID)
• [ ] Specification forks (lags, filtering, controls) tested with baseline shown alongside
• [ ] Method cross-check (SVAR↔LP, or perturbation↔global) where both are plausible
• [ ] Quantitative: alternative targets/parameters move the headline within a stated range
• [ ] Placebo/falsification and at least one external-validity check
• [ ] Honest statement of where the result weakens, not just where it holds

Anti-patterns

• A wall of robustness tables that never restate the baseline, so movement is invisible
• Testing only the choices that confirm the result; omitting the obvious adversarial fork
• Ignoring the ZLB/COVID break in a sample that spans it
• Claiming robustness from one alternative specification
• Hiding a fragile headline behind a forest of irrelevant checks
• "Available upon request" instead of an online-appendix robustness section

Worked vignette: is the fiscal multiplier a Great-Moderation artifact? (illustrative)

A paper reports a fiscal multiplier of 1.2 from a proxy-VAR on 1960–2019. A referee suspects it is driven by the volatile pre-1984 period. The robustness program: re-estimate on 1984–2019, exclude the ZLB years, and corroborate with local projections using the same narrative instrument. Suppose the multiplier is 1.2 full sample, 1.0 post-1984, 1.4 at the ZLB, all with overlapping bands, and the LP cross-check agrees within 0.1 — the paper then claims a multiplier "around 1.0