/aejmac-identification

name: aejmac-identification
description: Use when the empirical identification of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR, local projections, narrative, high-frequency/proxy-VAR, or micro-data macro designs. Stress-tests the identification to the AEJ: Macro broad-interest quantitative bar; for model-parameter identification see aejmac-theory-model.

Empirical Identification (aejmac-identification)

When to trigger

• The macro effect rests on a recursive (Cholesky) SVAR with no defense of the ordering
• A monetary/fiscal "shock" is plausibly anticipated or endogenous to the cycle
• Local projections are run but lag length, controls, and inference are ad hoc
• A narrative or high-frequency instrument is used but its exogeneity/relevance is unargued
• You are unsure the design clears AEJ: Macro's identified-empirical bar

The AEJ: Macro identification bar

AEJ: Macro publishes identified-empirical macro, so the **mapping from data to the dynamic causal object** (an impulse response, a multiplier, a pass-through) must be explicit and defended. The aggregate, time-series setting makes identification harder than in micro: few effective observations, anticipation, simultaneity, and structural breaks. State the **shock you claim to identify**, the **assumption that delivers it**, and the **horizon and object** you report. Report **standard errors / confidence bands** (the AEA house style; significance asterisks are conventional in AEA tables but the band/SE must carry the inference, not the stars).

Branch paths

Branch A: Structural VAR (SVAR)

• **Recursive (Cholesky):** defend the ordering as an economic timing assumption, not a default; show robustness to plausible reorderings.
• **Sign restrictions:** state the full set; acknowledge set-identification (report the identified set / median-target with a credible band, not a point as if point-identified); address the "multiple models" critique.
• **Long-run / Blanchard–Quah:** justify the long-run neutrality assumption.
• **Proxy-VAR / external instruments (SVAR-IV):** show instrument relevance (reliability/F) and defend exogeneity; report weak-instrument-robust bands where relevance is marginal.

Branch B: Local projections (LP)

• Report the **horizon-by-horizon** IRF with bands; state lag length and control set and show robustness to them.
• Use **Newey–West / HAC or LP-specific** inference; for panel LP cluster appropriately.
• Consider **LP-IV** when the shock needs instrumenting; report the first-stage strength.
• Address the LP-vs-VAR bias/variance trade-off explicitly if both are plausible.

Branch C: Narrative & high-frequency identification

• **Narrative shocks** (Romer–Romer style monetary/fiscal/tax): document the construction, the source record, and why the series is exogenous to the cycle; show it is unpredictable from macro history.
• **High-frequency monetary surprises** (event-window around announcements): defend the window, address the "Fed information effect" (orthogonalize against forecasts or use the information-robust instruments), report relevance.

Branch D: Micro-data macro / cross-sectional identification

• Cross-sectional or regional designs aggregated to a macro statement (e.g., regional multipliers): state the **general-equilibrium vs. partial-equilibrium** gap and how you map the cross-sectional elasticity to the aggregate.
• Use modern heterogeneity-robust estimators where staggered timing applies; cluster at the assignment level.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the identification claim, don't only argue 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.

1. `detect_design` → `recommend` → fit with `as_handle=true` → `audit_result` to list the checks the design still owes. 2. **Staggered DiD:** `callaway_santanna` / `sun_abraham` + `bacon_decomposition` + `honest_did_from_result` (the pre-trend test is low-power, Roth 2022). 3. **IV:** `effective_f_test` + an `anderson_rubin_ci` (valid under weak instruments), not a 2SLS t-stat alone. 4. **RDD:** `rdrobust` (bias-corrected) + `rddensity` / `mccrary_test` for manipulation. 5. **OVB:** `oster_delta` / `sensemakr` — how strong a confounder would have to be.

Report the economic magnitude; route the full battery to the appendix; keep every number reproducible. A run end-to-end (synthetic data, real returns) is in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md). If StatsPAI/Stata are not connected, adapt the vendored `resources/code/` skeleton and flag any unverified number.

Checklist

• [ ] Branch chosen; the shock and the data-to-IRF mapping stated in one sentence
• [ ] SVAR: ordering / sign set / long-run / proxy assumption defended, not defaulted
• [ ] LP: lag length, controls, HAC inference stated; robustness to them shown
• [ ] Narrative/HF: construction documented; exogeneity (unpredictability) demonstrated; relevance reported
• [ ] Cross-sectional-to-aggregate: PE-vs-GE gap addressed
• [ ] Inference: bands/SEs carry the conclusion; weak-instrument-robust where relevant
• [ ] The macro claim never exceeds the horizon/object the design identifies

Anti-patterns

• A Cholesky ordering presented as if it were innocuous, with no economic timing argument
• Sign-restricted IRFs reported as point estimates, hiding set-identification
• A "monetary shock" that is predictable from the prior quarter's data (anticipation not addressed)
• High-frequency surprises used without confronting the Fed information effect
• LP reported at a single cherry-picked horizon instead of the full respon