Jimf Identification

Identification Strategy (jimf-identification)

When to trigger

• A cross-country result rests on OLS-plus-controls and a referee will call it a correlation
• A high-frequency event study's "surprise" measure may be contaminated (information channel, anticipation, overlapping windows)
• A capital-control / FX-intervention / regime-switch design lacks a credible control group
• An open-economy model is estimated but it is unclear what in the data identifies each parameter
• You are unsure the design clears JIMF's bar for an international causal claim

The JIMF identification bar

JIMF judges identification through an open-economy lens: the threat to causality usually comes from a global confounder (the global financial cycle, a common US monetary shock, world risk appetite) or from policy endogeneity (countries impose controls or intervene precisely when flows or the exchange rate move). State the data-to-object mapping in one sentence, then defend it against the international-specific confounder. Pick the branch.

Branch A: High-frequency / surprise identification (FX, policy spillovers)

• Construct the surprise cleanly: narrow event windows (intraday or daily), surprises measured from futures/swaps (e.g. fed funds futures, OIS), and a window short enough to exclude other news.
• Separate the monetary shock from the information shock: a Fed/ECB announcement moves both policy expectations and the central bank's signal about the economy; show the sign/co-movement test (e.g. with stock prices) or use a poor-man's information-robust shock. JIMF referees now expect this.
• Spillover design: regress foreign asset prices / flows on the cleaned foreign (usually US) surprise in the tight window; cluster appropriately; report the first-stage strength if used as an IV.

Branch B: Cross-country panel causal design

• Push vs. pull: isolate the global push (common foreign factor) from country pull (domestic fundamentals) — interact the global shock with ex-ante country exposure rather than running a pooled regression that conflates them.
• Staggered policy adoption (capital controls, macroprudential, regime change): move beyond TWFE (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille); show event-study leads and a Goodman–Bacon decomposition; the control group must be plausibly unaffected by the same global shock.
• Endogenous policy: argue why the control/intervention timing is not a response to the outcome; use an instrument (e.g. exposure shares interacted with a global shock — a Bartik/shift-share, defended on the shares or the shocks) or an institutional discontinuity.

Branch C: Capital-control / FX-intervention natural experiment

• Define treatment date and intensity precisely; identify a comparison set of countries or assets not subject to the measure but exposed to the same external environment.
• Address anticipation and circumvention (controls leak; interventions are sterilized); test pre-trends and placebo countries/assets.

Branch D: Open-economy model / parameter identification

• Tie each structural parameter to an identifying data feature or moment (e.g. UIP deviation pins the risk-premium parameter; the term-structure slope pins persistence); report a sensitivity matrix and Monte Carlo recovery.
• Argue counterfactual / policy-invariance for the exchange-rate or capital-flow experiment you run (Lucas critique under a regime change).

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. JIMF is international macro-finance; cross-country panels + asset pricing — identification plus factor/Newey-West inference.

• detect_design → recommend → fit with as_handle=true → audit_result.
• Observational causal claims: staggered DiD (callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result); IV (effective_f_test + anderson_rubin_ci); RDD (rdrobust + mccrary_test).
• Experiments: randomization-based inference + romano_wolf for many-outcome control.
• Sensitivity: oster_delta / sensemakr for observational claims.

Report the magnitude in interpretable units; route the full battery to the appendix. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Checklist

• Branch chosen; data-to-object mapping stated in one sentence
• The global confounder (GFCy / common US shock / world risk) is named and addressed, not ignored
• High-frequency: window justified; monetary vs. information shock separated; surprise source stated
• Panel: push/pull separated; modern estimator where staggered TWFE would bias; control group not hit by the same global shock
• Policy experiment: endogenous-timing concern argued; anticipation/leakage tested; placebos shown
• Inference clusters at the right level (country, time, or two-way); few-cluster cross-country inference handled (wild bootstrap / Driscoll–Kraay where serial/cross correlation matters)
• Push-pull made explicit where relevant: global shock × predetermined country exposure, with time FE
• Exposure / instrument variables are predetermined (lagged/pre-sample), not contemporaneous
• The claim never exceeds what the design supports (correlation labeled as correlation)

Anti-patterns

• A pooled cross-country regression that conflates global push with country pull and calls the coefficient causal
• Treating an announcement-day return as a clean monetary shock without addressing the central-bank information channel
• Staggered capital-control adoption analyzed with plain TWFE and no heterogeneity-bias discussion
• A control group of countries that were hit by the same global financial-cycle shock as the treated group
• Ignoring that capital controls leak and FX intervention is often sterilized, then interpreting a null as "no effect"
• Calibrating an open-economy model and running a regime-change counterfactual with no invariance argument