Empirical Design (finman-empirical-design)

When to trigger

• The sample comes from CRSP / Compustat / a vendor feed and the screens and survivorship choices are not documented
• A key variable (leverage, payout, governance index, a return measure) has several definitions and you picked one without justification
• The panel mixes frequencies, has look-ahead bias, or merges datasets on a fragile key
• Standard errors are reported but the clustering and cross-sectional/time dependence are not justified

The FM empirical-design bar

FM publishes empirical finance across corporate, asset-pricing, and banking data, so the design layer is judged on whether a competent referee could reconstruct your sample and trust your measures. The journal's "less weight on trivial robustness" stance is a double-edged sword: it means you should not bury the paper in redundant checks, but it raises the premium on getting the primary design right the first time — the screens, the variable definitions, the merge, and the inference. FM referees in corporate finance are especially alert to silent sample screens, point-in-time vs. restated accounting data, and clustering that ignores the panel's dependence structure.

The data-layer audit

Hardening sequence

1. Build the attrition table. Start from the raw universe and report the count dropped at each screen; this single exhibit answers most sample-construction doubts.
2. Pin every key variable to a source field and a definition. State winsorization (typically 1%/99%) and why; if a variable has competing definitions, justify yours and note the alternative goes to the appendix.
3. Defend point-in-time discipline. For accounting variables, use data as it would have been known; for returns, avoid look-ahead in signal construction.
4. Justify the clustering. Cluster at the level where the shocks are correlated (firm, industry, state); use two-way (firm and time) when both dimensions have common shocks; address few-cluster with wild-cluster bootstrap.
5. Report power/economic scale. State N, the dependent-variable mean, and the standard-deviation-scaled effect so a reader sees the magnitude in context.

Execution bridge (StatsPAI / Stata MCP)

Run the asset-pricing battery, don't just specify it. Full map: execution-with-mcp. Financial Management is empirical corporate finance + asset pricing; corporate-causal chain (DiD/IV/RDD) plus the factor-zoo haircut for cross-sectional pricing.

• Factor regressions / time-series alphas: feols with the right SEs (Newey–West /

clustered) — read the alpha and t off the return.

• Factor-zoo haircut: after disclosing how many signals were screened, apply

romano_wolf / benjamini_hochberg and report the alpha that survives.

• Fama–MacBeth + Shanken EIV are Stata-canonical — run via mcp__stata-mcp__stata_do

with the vendored resources/code/ (asreg / xtfmb).

• Exhibits: etable; hand formatting to the tables/figures skill.

Report the economic magnitude (bps/month alpha, Sharpe gain); full factor grid → appendix. JF execution walkthrough.

Checklist

• [ ] Attrition table from raw universe to estimation sample, with counts per screen
• [ ] Every key variable defined, winsorization stated, source field named
• [ ] Survivorship and look-ahead bias addressed (point-in-time accounting; clean signals)
• [ ] Merge keys and match rate reported; unmatched-firm bias discussed
• [ ] Panel frequency and balanced/unbalanced status stated; entry/exit handled
• [ ] Clustering level justified; two-way / few-cluster handled where needed
• [ ] Dependent-variable mean and N reported so magnitudes are interpretable

Anti-patterns

• "We apply standard filters" with no attrition table or counts
• Restated accounting data used as if it were known at the time (look-ahead)
• A leverage / payout / governance measure that silently switches definition across tables
• CRSP-Compustat (or vendor) merges with an unreported or low match rate
• White / homoskedastic SEs on a firm-year panel with obvious serial and cross-sectional dependence
• Reporting only t-statistics with no dependent-variable mean to anchor the magnitude

Worked vignette (illustrative)

A draft studies payout on a "standard Compustat sample" with white standard errors. A referee cannot reconstruct it. The FM fix: add Table 1 Panel A as an attrition table (raw universe → drop financials/utilities → drop missing payout → final N), define payout precisely as dividends-plus-repurchases over assets winsorized at 1%/99%, switch to standard errors clustered by firm and year (the panel has both firm persistence and common market shocks), and report the dependent-variable mean so the coefficient's economic size is legible. The design is now reconstructable and the inference defensible.

Data-source notes specific to finance

• Compustat: beware restated data — use point-in-time (PIT) snapshots for accounting variables that signals are built from; flag any look-ahead in the merge.
• CRSP: retain delisted securities and apply delisting returns; survivorship bias from dropping them inflates many results.
• CRSP–Compustat link: report the link table used and the match rate; unmatched firms skew toward small/young/foreign issuers.
• Vendor / hand-collected data (governance, syndicated loans, microstructure): describe coverage, the time window, and any sample selection the vendor's universe imposes — FM referees ask "what is not in this dataset?"
• Returns: state whether returns are gross or net, the holding-period convention, and how microcaps/penny stocks are treated.

Referee pushback mapped to the design fix

• "I can't reproduce your sample." → Add the attrition table from the raw universe with counts dropped per screen.
• "Your accounting variable uses restated data." → Switch to point-in-time data and say so in the note.
• "The standard errors look too small." → Justify and report two-way (or wild-cluster) standard errors matched to the panel's dependence.
• "Is this effect economically meaningful?" → Report the dependent-variable mean and a one-SD-scaled effect.

When the design choice is itself the contribution

Some FM papers earn their place through a measurement or sample-construction innovation — a cleaner proxy, a newly merged dataset, a hand-collected sample. When that is the contribution:

• Validate the new measure against an external benchmark or a known case, not just internal consistency.
• Show what it captures that prior proxies miss, with a side-by-side comparison.
• Document construction in painstaking detail in the internet appendix, because the measure is the asset and referees will probe it.
• Connect the measurement gain to a substantive finding — a better proxy is interesting at FM only if it changes what we conclude about a decision-relevant question.

Output format

【Sample frame】universe + date range + screens (attrition table? [Y/N])
【Key variables】defined + winsorized + source fields named? [Y/N]
【Bias controls】survivorship / look-ahead handled? [Y/N]
【Merge】keys + match rate reported? [Y/N]
【Inference】clustering level justified; two-way/few-cluster handled? [Y/N]
【Magnitude】dep-var mean + N reported? [Y/N]
【Next skill】finman-robustness