tar-data-analysis

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](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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) or

benjamini_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

exact suggest_function for each.

• Exhibits: etable / did_summary_to_latex from 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

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