Agent Skills
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tar-data-analysis
GitHub用于执行并报告TAR稿件的估计分析，包括选择合适估计量、聚类标准误、固定效应及稳健性检验。同时负责构建符合TAR要求的数据真实性与代码访问文档，确保结果可复现且经得起审查。
Trigger Scenarios
样本构建完成需进行估计和报告时
不确定标准误、固定效应或估计量是否匹配设计时
审稿人质疑稳健性或样本筛选过程时
需要组装TAR要求的数据真实性/代码访问包时
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill tar-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "tar-data-analysis",
"description": "Use when running and reporting the estimation for a The Accounting Review (TAR) manuscript — the estimator, fixed effects, standard-error clustering, robustness, and the data-authenticity \/ code-access documentation TAR requires. Executes and reports the analysis; it does not design identification (tar-methods) or frame the contribution (tar-contribution-framing)."
}
Estimation, Robustness & Data Authenticity (tar-data-analysis)
When to trigger
- The sample is built and it is time to estimate and report
- You are unsure whether your standard errors, fixed effects, or estimator match the design
- Reviewers will probe robustness, alternative measures, or sample-selection screens
- You must assemble the data-authenticity / code-access package TAR requires
- A reviewer says "the result is not robust" or "I cannot tell how the sample was built"
Estimator and inference (large-sample archival core)
- Match the estimator to the design set in
tar-methods: OLS with high-dimensional fixed effects (firm, year, industry-year) for panel associations; DiD / staggered-DiD with a modern estimator for adoption shocks; 2SLS for endogenous regressors; RDD for threshold settings; logit/ probit/Poisson/Tobit for limited or count outcomes (e.g., restatement, going-concern, fraud). - Cluster standard errors at the level of treatment assignment / correlation (firm, or two-way firm-and-year); for few clusters use the wild-cluster bootstrap.
- Report fixed effects explicitly and show how the coefficient moves as you add them — a result that survives tighter fixed effects is more credible than one that does not.
- For accounting-specific measures (discretionary accruals, real earnings management, abnormal audit fees, effective/cash tax rates, disclosure tone), state the construction model and screen, and show the result is not an artifact of the proxy.
Robustness reviewers expect
- Alternative measures of the focal accounting construct (e.g., a second accruals model; cash vs. GAAP ETR; alternative disclosure proxy).
- Alternative samples and screens (drop financials/utilities; winsorize vs. truncate; subperiods).
- Sensitivity of the identifying assumption (pre-trends, placebo dates, alternative instruments, bandwidth choices for RDD).
- Falsification / placebo tests where the effect should be absent.
- Economic magnitude, not just significance — interpret the coefficient in accounting terms.
Data-authenticity & code access (a TAR-specific requirement)
TAR requires authors to enable confirmation of data authenticity, with differentiated rules:
- Publicly available databases (Compustat, CRSP, I/B/E/S, Audit Analytics): provide a precise description of the data and access to the computer code used to process it.
- Data abstracted from public sources (hand-collected from filings, PCAOB reports): provide the abstraction methodology plus code access.
- Privately collected data (proprietary field data, experiments): provide enough detail for reader confidence; corroborating third parties are acceptable.
Code/data sufficiency is part of the submission and acceptance requirements (待核实 whether a named public repository deposit is mandated at acceptance). Build clean, commented scripts from raw extract to every table now — not after the R&R.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. TAR is archival accounting — DiD around regulation / standard changes, IV, and earnings-based designs; the corporate-causal chain fits directly.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.
Checklist
- Estimator matches the design; FEs reported; SEs clustered at the right level
- Coefficient stability shown across FE/control sets
- Focal accounting measure validated with an alternative construction
- Pre-trends/placebo/falsification tests reported where the design needs them
- Economic magnitude interpreted, not just p-values
- Data-authenticity package assembled per data type (description + processing code / methodology)
- Sample-construction screens documented and reproducible from raw data
Anti-patterns
- Uncluttered p-values, no magnitude — significance without economic interpretation.
- Single proxy for a contested construct (e.g., one accruals model) presented as definitive.
- TWFE on staggered adoption ignoring heterogeneous-treatment-effect bias.
- Robustness theater: many tables that never vary the thing a skeptic doubts.
- Unreproducible sample: screens and merges that no one can rebuild from the raw data.
- No processing code ready, in violation of the data-authenticity policy.
Output format
【Estimator】OLS-HDFE / staggered-DiD / 2SLS / RDD / logit-Poisson ...
【Fixed effects & clustering】... ; SE level ...
【Focal measure】construction + alternative proxy: pass/issues
【Robustness】alt measures / samples / placebo / pre-trends ...
【Economic magnitude】coefficient means ... in accounting terms
【Data authenticity】public-db / abstracted / private — code & description ready? yes/no
【Open issues for reviewers】...
【Next step】tar-contribution-framing
Version History
- 1839142 Current 2026-07-05 14:28
