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finman-empirical-design

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Use when the sample construction, variable measurement, panel structure, or inference of a Financial Management (FM) manuscript is fragile — before identification can be trusted or exhibits finalized. Hardens the data layer; it does not establish the causal claim (finman-identification) or run robustness (finman-robustness).

Financial-Management-Skills/skills/finman-empirical-design/SKILL.md

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# 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
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