Agent Skills
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› car-methods
car-methods
GitHub为当代会计研究(CAR)论文提供研究设计与方法论指导。协助选择并论证合适的研究设计（档案、实验、分析等），确保内部效度与识别策略，特别强调涉及人类参与者时必须满足的伦理审批要求，以提升稿件的方法严谨性。
Trigger Scenarios
需要确定或辩护研究设计时
存在内生性问题且缺乏识别策略时
实验操纵可能无法隔离理论构念时
涉及人类参与者但尚未准备伦理审批时
审稿人质疑设计无法支持结论时
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill car-methods -g -y
SKILL.md
Frontmatter
{
"name": "car-methods",
"description": "Use when the research design and identification are the bottleneck for a Contemporary Accounting Research (CAR) manuscript — choosing and defending an archival, experimental, analytical, field, or survey design and, for human-participant work, meeting CAR's mandatory ethics-approval verification. Designs the study; it does not run the estimation (car-data-analysis)."
}
Research Design & Methods (car-methods)
When to trigger
- The design may not deliver the inference the question needs (identification, internal validity, equilibrium logic)
- An archival causal claim rests on an endogenous regressor with no strategy
- An experiment's manipulation may not isolate the theorized construct
- The study involves human participants and ethics-approval verification is unprepared
- A reviewer says "the design cannot support this claim"
Match the design to the question (CAR is method-agnostic)
CAR welcomes any appropriate method; the bar is fit and rigor, not a preferred method. Pick the design that earns the claim:
| Claim | Design that earns it |
|---|---|
| Capital-market/contracting effect of reporting | Panel archival with fixed effects + an identification strategy |
| Causal effect of an information feature on judgment | Controlled experiment (lab/online/professional subjects) |
| Existence/optimality of an equilibrium or contract | Analytical model: primitives, equilibrium concept, proofs |
| Mechanism inside firms, audits, or standard-setting | Field study / interviews with an explicit coding protocol |
| A new construct's measurement and external validity | Survey with a validated instrument; or multi-method |
A two-study design (e.g., an experiment isolating the mechanism behind an archival association) is a recognized CAR strength.
Design against the threats CAR reviewers probe
- Identification (archival). Anticipate omitted variables, reverse causality, and selection; plan a strategy (natural experiment, difference-in-differences with a credible parallel-trends argument, instrument, entropy balancing/matching, firm/year fixed effects) and state the assumptions each requires.
- Internal validity (experimental). Design manipulation and attention checks; randomize; pre-specify the predicted mediator; rule out demand effects and confounds; justify the participant pool (student, online, or professional) for the inference.
- Model discipline (analytical). Justify each assumption and the equilibrium concept; show which results are robust to relaxing assumptions.
CAR-specific design requirements
- Ethics-approval verification (mandatory). For any research involving human participants — experiments, interviews, surveys, including secondary human-participant data — you must obtain and upload institutional REB/IRB clearance, an REB-issued exemption, or a senior-administrator letter where no review board exists. A bare assertion is not accepted, and failure is grounds for withdrawal by the EIC. Plan this before data collection.
- Instrument capture. Surveys/experiments must submit the full research instrument with the manuscript (Data Integrity policy, item 1).
- Proprietary/field data. If you use proprietary organizational data, plan a credible means of verifying the data source/site on editor request and disclose any non-disclosure restrictions (policy item 2).
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. Full
map: execution-with-mcp. CAR is archival/empirical accounting; the DiD / IV / RDD chain serves its causal designs around reporting and regulation.
detect_design→recommend→ fit withas_handle=true→audit_resultto enumerate the checks the design owes.- Panel / staggered DiD:
callaway_santanna/sun_abraham+bacon_decompositionhonest_did_from_result. IV:effective_f_test+anderson_rubin_ci. RDD:rdrobust+mccrary_test.
- Experiments: randomization-based inference and
romano_wolffor 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
- Design can support each prediction (identification / internal validity / equilibrium logic)
- (Archival) endogeneity strategy specified with its assumptions
- (Experimental) manipulation/attention checks, randomization, predicted mediator, pool justified
- (Analytical) assumptions and equilibrium concept justified; robustness mapped
- Ethics-approval verification secured for any human participants
- Full instrument prepared; proprietary-data verification/NDA plan in place
Anti-patterns
- Cross-sectional causal claims from one-period archival correlations with no strategy.
- Confounded manipulations that move more than the theorized construct.
- Assumption-driven results (analytical) never tested for robustness.
- Treating ethics approval as a formality — CAR requires documented verification, not a statement.
Output format
【Design】panel-archival / experiment / analytical / field / survey / multi-method
【Inference fit】each prediction supportable? notes ...
【Identification / internal validity / equilibrium】strategy + assumptions ...
【Ethics】REB/IRB clearance, exemption, or senior-admin letter secured?
【Instrument & proprietary data】full instrument; verification/NDA plan ...
【Next step】car-data-analysis
Version History
- 1839142 Current 2026-07-05 12:47
