| name | jf-identification |
| description | Use when the causal-identification strategy is the bottleneck for a corporate / empirical The Journal of Finance (JF) manuscript — natural experiments, IV, DID, RDD. Stress-tests the design; for asset-pricing tests use jf-empirical-design. |
Causal Identification (jf-identification)
When to trigger
- The paper makes a causal claim ("X causes Y") resting on a research design
- You rely on an instrument, a shock, a discontinuity, or a diff-in-diff and a referee will attack the exclusion/parallel-trends assumption
- Endogeneity (reverse causality, omitted variables, selection) threatens the headline result
Scope: corporate / empirical causal effects. For cross-sectional asset-pricing tests use jf-empirical-design.
JF's bar for identification
JF is the AFA flagship, general-interest, with a ~5% acceptance rate and ~33–45% desk rejection (afajof.org editor reports, accessed 2026-05-30). For a corporate/empirical paper, credible identification is usually the binding constraint — a clever question with a weak design is a classic JF desk reject. The design must convince a broad AFA readership, not just specialists.
Design audit
| Design | Core assumption to defend | Standard JF attack to pre-empt |
|---|
| Natural experiment | Shock is plausibly exogenous & well-timed | Anticipation; confounding co-occurring events |
| Instrumental variables | Relevance + exclusion | "Why does the instrument affect Y only via X?" |
| Diff-in-diff | Parallel trends; no differential shocks | Pre-trends; staggered-adoption bias |
| RDD | No manipulation; continuity at the cutoff | Bunching; bandwidth sensitivity |
- State the source of variation in one sentence in the introduction (JF rewards a clearly named shock or instrument).
- Show the identifying assumption is testable where possible (pre-trends, first-stage F, McCrary test) and put the full battery in the Internet Appendix (bundled in the same PDF; see
jf-internet-appendix).
- Report economic magnitude, since JF writes for a general-interest reader.
Worked vignette — a staggered-regulation natural experiment
Illustrative numbers. A paper claims a disclosure regulation, rolled out across states in 2011–2016, causes treated firms to cut leverage; the DID shows book leverage falling 4.2 pp (t = 3.4). Walk it through JF's bar:
- Name the variation in one sentence: "Staggered state-level adoption of Rule X gives treated firms a plausibly exogenous shock to disclosure costs" — the introduction's credibility hook for a broad-readership editor.
- Defend the assumption: an event-study plot shows flat pre-trends before adoption; the full coefficient panel goes to the Internet Appendix.
- Fix the staggered-adoption bias: a naive two-way fixed-effects estimate (4.2 pp) is contaminated by already-treated controls. Re-estimate with a modern estimator (Callaway–Sant'Anna or Sun–Abraham); the clean estimate lands at ~3.1 pp — report it and flag the TWFE bias.
- Pre-empt anticipation: show no effect in the year before the law as a placebo.
- Report magnitude: 3.1 pp on a ~30% mean is a ~10% relative move — say so, since JF prizes economically large effects over bare significance.
The editor sees a named shock, a defended assumption, the right estimator, and a magnitude that matters to the AFA readership.
Referee-pushback patterns and the JF-specific fix
| Pushback you will hear | JF-specific fix |
|---|
| "Your TWFE DID is biased under staggered adoption" | Re-estimate with Callaway–Sant'Anna / Sun–Abraham; show both |
| "The instrument could affect Y through other channels" | Spell out the one channel; falsification on the alternative paths |
| "Treated and control firms differ at baseline" | Balance table + covariate-trend plot in the Internet Appendix |
| "The shock coincides with the 2014–16 oil bust" | Excluded-period re-estimation; industry × year fixed effects |
| "Is 3 points economically meaningful?" | Express as % of the sample mean and tie to a dollar magnitude |
Calibration anchors for JF identification
- For a corporate/empirical paper, identification is typically the binding constraint: a first-order question with a fragile design is a classic flagship desk reject, while a less novel question with airtight identification can survive.
- JF expects the identification battery visible but not bloating the body — pre-trends, first-stage F, McCrary density, balance tables go to the Internet Appendix, with one or two decisive plots in the main text.
- Weak-instrument and modern-DID standards evolve; confirm the expected diagnostics against recent issues and current author guidelines.
Execution bridge (StatsPAI / Stata MCP)
Do not stop at advising the right estimator — run it and report the number. Full
map: shared-resources/empirical-methods/execution-with-mcp.md. JF-specific instantiation:
detect_design → preflight → recommend on the data; fit with as_handle=true.- Staggered DiD: estimate with
callaway_santanna / sun_abraham (not bare TWFE);
run bacon_decomposition to expose the bad-comparison weight you are correcting —
this is the "TWFE is biased" pre-emption, executed. Put the clean estimate in the
body; the event-study/pre-trend panel goes to the Internet Appendix.
- IV: report
effective_f_test and an anderson_rubin_ci (weak-IV-robust), not a
2SLS t-stat alone.
- RDD:
rdrobust for the bias-corrected estimate; rddensity/mccrary_test and
bandwidth sweep (rdbwselect) → Internet Appendix; one density/RD plot in the body.
audit_result(result_id) to enumerate what the design still owes; then
honest_did_from_result to bound a pre-trend violation. Cite methods only via
bibtex.
The JF body shows one or two decisive exhibits with the economic magnitude; the
full diagnostic battery lives in the bundled Internet Appendix (see jf-internet-appendix).
If StatsPAI/Stata are not connected, adapt the vendored resources/code/ skeleton and
say which number is unverified.
See this run end-to-end on synthetic data — every number an actual tool return — in
resources/worked-examples/02-execution-walkthrough.md
(TWFE −0.0227 vs clean CS −0.0272, pre-trends p = 0.155, honest-DiD breakdown point).
Checklist
Anti-patterns
- A causal verb ("increases", "causes") with only conditional correlations behind it
- An instrument with a hand-waved exclusion restriction
- Two-way fixed-effects DID on staggered adoption with no modern correction
- A clever question whose design no broad-readership editor would send out
Output format
【Design】NE / IV / DID / RDD
【Source of variation (1 sentence)】...
【Key assumption + how defended】...
【Main threat pre-empted?】yes / no
【Magnitude】...
【Next step】jf-robustness
