Research Design & Identification (jar-methods)
When to trigger
- The design is a panel regression with no source of identifying variation
- The claim is causal but the variation is observational/endogenous
- A referee says "this is correlation, not causation" or "the channel is unidentified"
- You must choose between an archival, experimental, analytical, or field design
JAR's dominant design: empirical-archival capital markets
JAR's defining methodology is large-sample empirical-archival capital-markets research (financial-statement and market-data econometrics in the Ball-Brown lineage). The journal also publishes experimental, analytical/modeling, and field-study work, and the Registered Reports track is well suited to higher-outcome-risk designs that require new data collection. The bar across all of them is credible identification: a referee must believe the estimate reflects the economic effect you claim, not an omitted variable, reverse causality, or selection.
Find identifying variation (the core archival problem)
| Theoretical claim |
Identification that earns it |
| Effect of a rule/standard |
Staggered or sharp adoption as a natural experiment (modern DiD) |
| Effect at a threshold |
Regression discontinuity (e.g., index inclusion, size cutoffs) |
| Effect of an endogenous firm choice |
IV/2SLS with a defensible instrument, or a shock to the choice |
| Information content of a disclosure |
Short-window event study around the release |
| Causal mechanism under control |
Experiment (lab/online/field), often paired with archival evidence |
- Anticipate the threats by name: omitted correlated variables, reverse causality, selection into treatment, and measurement error. State which one is the binding concern and how the design neutralizes it.
- Parallel trends / pre-trends for DiD; bandwidth and manipulation tests for RD; instrument relevance and exclusion for IV — plan the diagnostics now, not in the rebuttal.
- Construct measurement: define accounting constructs (earnings quality, disclosure, audit quality, comparability) with measures used in prior JAR work, and plan validation.
Sample and reproducibility from the start
Specify the sample frame, screens, and data vintages (Compustat/CRSP/I/B/E/S/Audit Analytics/EDGAR). Because JAR requires posted data and code, design the pipeline to be top-to-bottom reproducible from raw extracts, recording exclusion rules and access dates.
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. Full
map: execution-with-mcp. JAR is archival/empirical accounting; foreground identification around disclosure and regulation shocks, with modern DiD where adoption is staggered.
detect_design → recommend → fit with as_handle=true → audit_result to
enumerate the checks the design owes.- Panel / 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 and
romano_wolf for the many-outcome
family-wise correction reviewers expect.
Match the toolchain to the reviewer pool, and report the effect size the venue
wants. A run end-to-end (synthetic data, real returns) is in the
JF execution walkthrough.
Checklist
Anti-patterns
- Kitchen-sink OLS with controls standing in for identification.
- Endogenous regressor treated as exogenous with no strategy.
- Weak/implausible instruments failing relevance or exclusion.
- Staggered DiD with two-way FE ignoring heterogeneous-treatment-timing bias (use modern estimators).
- Event windows fished for significance; ad hoc bandwidths in RD.
Output format
【Design】archival-NE / RD / IV / event-study / experiment / analytical / field
【Identifying variation】shock / threshold / instrument / manipulation
【Binding threat】OVB / reverse causality / selection / measurement — addressed by ...
【Diagnostics planned】pre-trends / bandwidth / first-stage / balance ...
【Constructs & measures】definitions + validation
【Sample & reproducibility】frame, screens, vintages; data/code pipeline
【Next step】jar-data-analysis
Resources
Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Accounting-Research-Skills/skills/jar-methods/SKILL.md
1---2name: jar-methods3description: Use when the research design and identification strategy are the bottleneck for a Journal of Accounting Research (JAR) manuscript — choosing the setting, source of identifying variation, and sample to support a causal accounting claim. Designs the study; it does not run the estimation, clustering, or robustness (jar-data-analysis).4---5
6
7# Research Design & Identification (jar-methods)
8
9## When to trigger
10
11- The design is a panel regression with no source of identifying variation
12- The claim is causal but the variation is observational/endogenous
13- A referee says "this is correlation, not causation" or "the channel is unidentified"
14- You must choose between an archival, experimental, analytical, or field design
15
16## JAR's dominant design: empirical-archival capital markets
17
18JAR's defining methodology is **large-sample empirical-archival capital-markets** research (financial-statement and market-data econometrics in the **Ball-Brown** lineage). The journal also publishes **experimental**, **analytical/modeling**, and **field-study** work, and the **Registered Reports** track is well suited to higher-outcome-risk designs that require new data collection. The bar across all of them is **credible identification**: a referee must believe the estimate reflects the economic effect you claim, not an omitted variable, reverse causality, or selection.
19
20## Find identifying variation (the core archival problem)
21
22| Theoretical claim | Identification that earns it |
23|-------------------|------------------------------|
24| Effect of a rule/standard | Staggered or sharp adoption as a natural experiment (modern DiD) |
25| Effect at a threshold | Regression discontinuity (e.g., index inclusion, size cutoffs) |
26| Effect of an endogenous firm choice | IV/2SLS with a defensible instrument, or a shock to the choice |
27| Information content of a disclosure | Short-window event study around the release |
28| Causal mechanism under control | Experiment (lab/online/field), often paired with archival evidence |
29
30- **Anticipate the threats by name**: omitted correlated variables, reverse causality, selection into treatment, and measurement error. State which one is the binding concern and how the design neutralizes it.
31- **Parallel trends / pre-trends** for DiD; **bandwidth and manipulation tests** for RD; **instrument relevance and exclusion** for IV — plan the diagnostics now, not in the rebuttal.
32- **Construct measurement**: define accounting constructs (earnings quality, disclosure, audit quality, comparability) with measures used in prior JAR work, and plan validation.
33
34## Sample and reproducibility from the start
35
36Specify the sample frame, screens, and data vintages (Compustat/CRSP/I/B/E/S/Audit Analytics/EDGAR). Because JAR **requires** posted data and code, design the pipeline to be top-to-bottom reproducible from raw extracts, recording exclusion rules and access dates.
37
38## Execution bridge (StatsPAI / Stata MCP)
39
40For the **empirical / causal lane**, estimate and audit rather than only specify. Full
41map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). JAR is archival/empirical accounting; foreground identification around disclosure and regulation shocks, with modern DiD where adoption is staggered.
42
43- `detect_design` → `recommend` → fit with `as_handle=true` → `audit_result` to
44 enumerate the checks the design owes.
45- **Panel / staggered DiD:** `callaway_santanna` / `sun_abraham` + `bacon_decomposition`
46 + `honest_did_from_result`. **IV:** `effective_f_test` + `anderson_rubin_ci`. **RDD:**
47 `rdrobust` + `mccrary_test`.
48- **Experiments:** randomization-based inference and `romano_wolf` for the many-outcome
49 family-wise correction reviewers expect.
50
51Match the toolchain to the **reviewer pool**, and report the effect size the venue
52wants. A run end-to-end (synthetic data, real returns) is in the
53[JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).
54## Checklist
55
56- [ ] A specific source of identifying variation is named (shock/threshold/instrument/manipulation)
57- [ ] The binding endogeneity threat is identified and the design addresses it
58- [ ] Diagnostics planned (pre-trends / bandwidth / first-stage / manipulation checks)
59- [ ] Accounting constructs measured with validated, prior-literature measures
60- [ ] Sample frame, screens, and data vintages specified; pipeline reproducible
61- [ ] If high-risk/new-data: Registered Reports (Stage 1 protocol) considered
62
63## Anti-patterns
64
65- **Kitchen-sink OLS** with controls standing in for identification.
66- **Endogenous regressor** treated as exogenous with no strategy.
67- **Weak/implausible instruments** failing relevance or exclusion.
68- **Staggered DiD with two-way FE** ignoring heterogeneous-treatment-timing bias (use modern estimators).
69- **Event windows fished** for significance; ad hoc bandwidths in RD.
70
71## Output format
72
73```
74【Design】archival-NE / RD / IV / event-study / experiment / analytical / field
75【Identifying variation】shock / threshold / instrument / manipulation
76【Binding threat】OVB / reverse causality / selection / measurement — addressed by ...
77【Diagnostics planned】pre-trends / bandwidth / first-stage / balance ...
78【Constructs & measures】definitions + validation
79【Sample & reproducibility】frame, screens, vintages; data/code pipeline
80【Next step】jar-data-analysis
81```
82
83## Resources
84
85- [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — official JAR/Chicago Booth/Wiley URLs (accessed 2026-06-01)
86- [`../../resources/external_tools.md`](../../resources/external_tools.md) — archival data sources and identification tooling (reghdfe / csdid / rdrobust / ivreghdfe)
87
88---
89
90**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Accounting-Research-Skills/skills/jar-methods/SKILL.md`
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