jae-data-analysis

Research

Use when running and reporting the empirical analysis for a Journal of Accounting and Economics (JAE) manuscript — building the archival sample, choosing fixed effects and clustered standard errors, executing the identification design, and demonstrating robustness for large-sample capital-markets/contracting/disclosure data. Executes and reports the analysis; it does not design the study (jae-methods) or frame the contribution (jae-contribution-framing).

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-Accounting-and-Economics-Skills/skills/jae-data-analysis/SKILL.md

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Data Analysis & Inference for JAE (jae-data-analysis)

When to trigger

The sample is built and it is time to estimate and report
You are unsure how to specify fixed effects or cluster standard errors
Reviewers will probe endogeneity, correlated omitted variables, or sample selection
A reviewer says "the standard errors are understated" or "this is not identified"

Build and document the archival sample first

JAE reviewers expect a transparent sample-construction waterfall: starting population (e.g., Compustat firm-years), each merge (CRSP, I/B/E/S, Execucomp, DealScan, Audit Analytics via WRDS), each exclusion (financials/utilities, missing data, penny stocks), and the final N at every step. Report descriptive statistics and a correlation table. Winsorize continuous variables (commonly at 1%/99%) and say so.

Specify the estimator to match the panel and the design

Data structure / claim	Estimator / specification
Firm panel with unobserved heterogeneity	Firm and year fixed effects (e.g., `reghdfe`)
Inference with within-firm correlation	Standard errors clustered by firm; often two-way (firm & year)
Regulatory shock / treatment	Difference-in-differences; report pre-trends
Endogenous regressor	2SLS/IV with first-stage diagnostics (F-stat, exclusion)
Self-selection	Heckman (report inverse Mills) or PSM (report balance)
Information event	Short-window CARs; cross-sectional regression of returns
Binary/limited outcome	Logit/probit/Tobit as the outcome dictates

Match the clustering to where correlation lives in the data; a single firm-clustered SE may understate inference when shocks are common across firms in a year — two-way clustering is the JAE norm for many panels.

Execute the identification, not just the regression

DiD: plot/test parallel pre-trends; report the dynamic (event-time) coefficients, not only the average treatment effect.
IV: report the first stage, the instrument's strength, and defend the exclusion restriction in words.
Matching/Heckman: report covariate balance or the selection equation; show results are not an artifact of the procedure.
Cross-sectional partitions: the theory's mechanism test — show the effect concentrates where the friction (information asymmetry, weak governance, tight covenants) is severe.

Robustness (expected, not optional)

Alternative proxies for the key construct (e.g., different discretionary-accruals or conservatism measures).
Alternative specifications (controls in/out, alternative fixed effects, subsamples).
Placebo/falsification tests and, for DiD, a non-event window.
Sensitivity to correlated omitted variables (e.g., bounding / coefficient-stability arguments).
Address economically plausible alternative explanations empirically.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JAE is empirical accounting with an economics lens; treat identification and weak-IV-robust inference as the binding constraints.

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.

Checklist

Sample waterfall with N at each step; winsorization stated
Descriptives and correlation table reported
Fixed effects and clustered (often two-way) SEs match the design
Identification executed (pre-trends / first stage / balance), not assumed
Cross-sectional partition supports the economic mechanism
Robustness: alternative proxies, specifications, placebos, sensitivity
Economic magnitude (not only significance) reported

Anti-patterns

Pooled OLS with no fixed effects or clustering on a firm panel.
One-way clustering when shocks are common across firms within a year.
Reporting an IV with no first stage or no exclusion-restriction defense.
DiD with no pre-trend evidence.
Significance with no economic magnitude ("statistically significant" but trivially small).
Selective controls that make the result appear.

Output format

【Sample】population → merges → exclusions → final N; winsorized at ...
【Specification】FE (firm/year); SE clustering (firm / two-way)
【Identification executed】pre-trends / first-stage F / balance ...
【Main result】coefficient, t-stat, economic magnitude
【Mechanism (cross-section)】effect concentrated where friction severe
【Robustness】alt proxies / specs / placebo / sensitivity
【Open issues for reviewers】...
【Next step】jae-contribution-framing