JFQA Identification Strategy (jfqa-identification-strategy)
Use this skill to make the research design defensible for JFQA, an empirical and quantitative finance journal. JFQA referees press hard on whether a correlation is causal (or, in asset pricing, whether a premium is robust and not data-mined).
Empirical finance designs (the common case)
Pick the design that matches the question and defend it:
- Cross-section of returns — portfolio sorts and Fama-MacBeth regressions; Newey-West / clustered SEs; control for standard factors; report economic magnitudes (return per one-SD change), not only t-stats; guard against data snooping (out-of-sample, multiple-testing awareness).
- Corporate finance panels — firm and time fixed effects, two-way clustering; show the variation that identifies the coefficient.
- Policy / regulatory shocks — staggered DID with a modern estimator (Callaway-Sant'Anna, de Chaisemartin-D'Haultfœuille), event-study leads/lags, and parallel-trends evidence; avoid naive TWFE on staggered timing.
- Natural experiments / IV — instrument relevance (first-stage F), exclusion logic backed by an economic story, weak-IV-robust CIs.
- Thresholds / index reconstitution — RDD with manipulation/density tests and bandwidth robustness.
- Announcements — event study with CARs/BHARs, a defensible market model, and attention to calendar clustering.
What referees demand
- The right standard errors (clustering dimension justified, two-way where needed).
- Economic significance reported alongside statistical significance.
- Endogeneity confronted explicitly, not waved away with "controls."
Theoretical submissions
JFQA also publishes theory. If your paper is a model, pivot this skill to: stating assumptions transparently, deriving results/propositions, clean proof exposition, and testable implications a finance reader can take to data. Keep generality matched to the question.
Threat-to-remedy matrix for the JFQA referee report
| Endogeneity threat |
How it surfaces in the draft |
JFQA-grade remedy |
| Reverse causality |
outcome plausibly drives the regressor |
timing structure, a shock that moves only the regressor, or an IV with an economic exclusion story |
| Omitted firm-level variation |
"we control for size and B/M" |
firm FE plus a within-firm variation count showing the coefficient is still identified |
| Selection into treatment |
treated and control firms differ pre-event |
matching or entropy balancing plus pre-trend evidence, not either alone |
| Anticipation of regulation |
effects appear before adoption |
shift the event date, drop the anticipation window, show announcement-date returns |
| Data-mined anomaly |
one sort, one sample, large t-stat |
sub-period splits, out-of-sample evidence, multiple-testing discussion |
| Bad controls |
post-treatment variables on the RHS |
re-specify; report with and without, and explain which is the estimand |
Worked vignette: staggered adoption done the JFQA way (illustrative)
Suppose 23 states adopt a disclosure rule between 2008 and 2016 and the outcome is the credit spread of in-state issuers. A naive TWFE regression gives -4.1%; the Callaway-Sant'Anna group-time ATT gives -2.6% because late-vs-already-treated comparisons inflated the TWFE number. The JFQA presentation: CS estimator as the headline, TWFE relegated to the appendix with the discrepancy explained, an event-study figure whose lead coefficients are jointly insignificant (p = 0.42), and — with only 23 clusters — wild cluster bootstrap inference (p = 0.03) instead of leaning on asymptotics. That package answers the three referee questions (estimator, pre-trends, inference) before they are asked.
The anomaly-credibility bar in asset pricing
- Acknowledge the multiple-testing problem head-on: the post-2016 factor-zoo literature argues for materially higher t-hurdles for new predictors; state how many specifications were examined.
- Show tradability: turnover, transaction-cost drag, and whether the premium survives value-weighting and the exclusion of microcaps.
- Run spanning tests against the standard factor models in current use; a new "factor" that the existing ones price is a robustness row, not a contribution.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the identification claim, don't only argue it. Full map:
execution-with-mcp. JFQA is empirical finance (asset pricing + corporate) — the DiD / IV / RDD chain for corporate causal claims, the factor-zoo haircut for cross-sectional pricing.
detect_design → recommend → fit with as_handle=true → audit_result to list
the checks the design still owes.- Staggered DiD:
callaway_santanna / sun_abraham + bacon_decomposition +
honest_did_from_result (the pre-trend test is low-power, Roth 2022).
- IV:
effective_f_test + an anderson_rubin_ci (valid under weak instruments),
not a 2SLS t-stat alone.
- RDD:
rdrobust (bias-corrected) + rddensity / mccrary_test for manipulation.
- 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. If StatsPAI/Stata are not connected, adapt the
vendored resources/code/ skeleton and flag any unverified number.
Output format
【Design】sorts/FMB / panel FE / staggered DID / IV / RDD / event study / theory
【Identifying variation】what makes it credible
【Inference】clustering / weak-IV / multiple-testing handling
【Economic magnitude】effect size in finance units
【Next step】jfqa-data-analysis
Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-identification-strategy/SKILL.md
1---2name: jfqa-identification-strategy3description: Use when building a credible identification / research design for a Journal of Financial and Quantitative Analysis (JFQA) empirical finance paper — portfolio sorts and Fama-MacBeth, panel fixed effects, staggered DID on regulatory shocks, IV / natural experiments, RDD at thresholds, and event studies — with the inference finance referees demand. For theoretical submissions, pivot to assumptions, results, and proof exposition.4---5
6
7# JFQA Identification Strategy (jfqa-identification-strategy)
8
9Use this skill to make the research design defensible for **JFQA**, an empirical and quantitative finance journal. JFQA referees press hard on whether a correlation is causal (or, in asset pricing, whether a premium is robust and not data-mined).
10
11## Empirical finance designs (the common case)
12
13Pick the design that matches the question and defend it:
14
15- **Cross-section of returns** — portfolio sorts and **Fama-MacBeth** regressions; Newey-West / clustered SEs; control for standard factors; report economic magnitudes (return per one-SD change), not only t-stats; guard against data snooping (out-of-sample, multiple-testing awareness).
16- **Corporate finance panels** — **firm and time fixed effects**, two-way clustering; show the variation that identifies the coefficient.
17- **Policy / regulatory shocks** — **staggered DID** with a modern estimator (Callaway-Sant'Anna, de Chaisemartin-D'Haultfœuille), event-study leads/lags, and parallel-trends evidence; avoid naive TWFE on staggered timing.
18- **Natural experiments / IV** — instrument relevance (first-stage F), exclusion logic backed by an economic story, weak-IV-robust CIs.
19- **Thresholds / index reconstitution** — **RDD** with manipulation/density tests and bandwidth robustness.
20- **Announcements** — **event study** with CARs/BHARs, a defensible market model, and attention to calendar clustering.
21
22## What referees demand
23
24- The right standard errors (clustering dimension justified, two-way where needed).
25- Economic significance reported alongside statistical significance.
26- Endogeneity confronted explicitly, not waved away with "controls."
27
28## Theoretical submissions
29
30JFQA also publishes theory. If your paper is a model, pivot this skill to: stating **assumptions** transparently, deriving **results/propositions**, clean **proof exposition**, and **testable implications** a finance reader can take to data. Keep generality matched to the question.
31
32## Threat-to-remedy matrix for the JFQA referee report
33
34| Endogeneity threat | How it surfaces in the draft | JFQA-grade remedy |
35|---|---|---|
36| Reverse causality | outcome plausibly drives the regressor | timing structure, a shock that moves only the regressor, or an IV with an economic exclusion story |
37| Omitted firm-level variation | "we control for size and B/M" | firm FE plus a within-firm variation count showing the coefficient is still identified |
38| Selection into treatment | treated and control firms differ pre-event | matching or entropy balancing **plus** pre-trend evidence, not either alone |
39| Anticipation of regulation | effects appear before adoption | shift the event date, drop the anticipation window, show announcement-date returns |
40| Data-mined anomaly | one sort, one sample, large t-stat | sub-period splits, out-of-sample evidence, multiple-testing discussion |
41| Bad controls | post-treatment variables on the RHS | re-specify; report with and without, and explain which is the estimand |
42
43## Worked vignette: staggered adoption done the JFQA way (illustrative)
44
45Suppose 23 states adopt a disclosure rule between 2008 and 2016 and the outcome is the credit spread of in-state issuers. A naive TWFE regression gives -4.1%; the Callaway-Sant'Anna group-time ATT gives -2.6% because late-vs-already-treated comparisons inflated the TWFE number. The JFQA presentation: CS estimator as the headline, TWFE relegated to the appendix with the discrepancy explained, an event-study figure whose lead coefficients are jointly insignificant (p = 0.42), and — with only 23 clusters — wild cluster bootstrap inference (p = 0.03) instead of leaning on asymptotics. That package answers the three referee questions (estimator, pre-trends, inference) before they are asked.
46
47## The anomaly-credibility bar in asset pricing
48
49- Acknowledge the multiple-testing problem head-on: the post-2016 factor-zoo literature argues for materially higher t-hurdles for new predictors; state how many specifications were examined.
50- Show tradability: turnover, transaction-cost drag, and whether the premium survives value-weighting and the exclusion of microcaps.
51- Run spanning tests against the standard factor models in current use; a new "factor" that the existing ones price is a robustness row, not a contribution.
52
53## Execution bridge (StatsPAI / Stata MCP)
54
55Estimate and audit the identification claim, don't only argue it. Full map:
56[`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). JFQA is empirical finance (asset pricing + corporate) — the DiD / IV / RDD chain for corporate causal claims, the factor-zoo haircut for cross-sectional pricing.
57
581. `detect_design` → `recommend` → fit with `as_handle=true` → `audit_result` to list
59 the checks the design still owes.
602. **Staggered DiD:** `callaway_santanna` / `sun_abraham` + `bacon_decomposition` +
61 `honest_did_from_result` (the pre-trend test is low-power, Roth 2022).
623. **IV:** `effective_f_test` + an `anderson_rubin_ci` (valid under weak instruments),
63 not a 2SLS t-stat alone.
644. **RDD:** `rdrobust` (bias-corrected) + `rddensity` / `mccrary_test` for manipulation.
655. **OVB:** `oster_delta` / `sensemakr` — how strong a confounder would have to be.
66
67Report the economic magnitude; route the full battery to the appendix; keep every
68number reproducible. A run end-to-end (synthetic data, real returns) is in the
69[JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md). If StatsPAI/Stata are not connected, adapt the
70vendored `resources/code/` skeleton and flag any unverified number.
71## Output format
72
73```
74【Design】sorts/FMB / panel FE / staggered DID / IV / RDD / event study / theory
75【Identifying variation】what makes it credible
76【Inference】clustering / weak-IV / multiple-testing handling
77【Economic magnitude】effect size in finance units
78【Next step】jfqa-data-analysis
79```
80
81---
82
83**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-identification-strategy/SKILL.md`
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