# Jfe Empirical Design > Use when settling the measurement and estimation choices of a Journal of Financial Economics (JFE) manuscript — factor construction, portfolio sorts, Fama-MacBeth/GMM, standard-error clustering, and multiple-testing discipline. Covers the design/estimator layer; for causal identification of corporate-finance effects use jfe-identification. - Skill: `thedixitjain/jfe-empirical-design` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/jfe-empirical-design` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/jfe-empirical-design/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/jfe-empirical-design --- # Empirical Design & Inference (jfe-empirical-design) ## When to trigger - You sort on a characteristic but have not justified the variable, the breakpoints, or weighting - You are choosing between Fama–MacBeth, panel regression, and GMM and unsure how to report - Your standard errors are unclustered, or clustered on one dimension when two are needed - You have an asset-pricing predictor but no out-of-sample or multiple-testing treatment - Variable definitions are ad hoc and would not replicate ## The JFE design bar JFE is known for nuts-and-bolts methodological rigor. Referees scrutinize measurement, estimator choice, standard errors, and inference discipline line by line. The goal is a design that a skeptical expert cannot dismantle on technical grounds. This is the journal that published **Fama & French (1993), "Common risk factors in the returns on stocks and bonds"** (the three-factor model), **Fama & French (2015), "A five-factor asset pricing model,"** and **Banz (1981), the size effect** — so an asset-pricing referee benchmarks your construction against that lineage directly. The best capital-markets paper each year wins JFE's **Fama-DFA Prize**; write to that standard. Code and non-proprietary data are mandatory at acceptance (Mendeley Data; see jfe-submission), so build a reproducible pipeline from the start. ## Asset pricing ### Factor / portfolio construction - Justify the sorting variable economically and define it precisely (data source, lag, winsorization). - State breakpoints (e.g., NYSE breakpoints vs. all-stock) and value- vs. equal-weighting, and show the choice does not drive the result. - Report turnover and whether the strategy survives plausible transaction costs. ### Cross-sectional inference - **Fama–MacBeth:** report Newey–West / Shanken-corrected standard errors; state lags. (Note Fama-MacBeth itself originates in *JPE* 1973; the factor-model machinery it serves is JFE's home turf via Fama-French.) - **GMM / SDF:** state moment conditions, weighting matrix, and over-identification (J-test). - Report alphas against the **Fama-French** benchmarks — CAPM, FF3, FF5 — plus momentum/q-factor where relevant, and show your factor survives **spanning regressions** against them. A single-benchmark alpha will not satisfy a JFE asset-pricing referee. ### Inference discipline - **Out-of-sample:** show the predictor holds out of sample or in a holdout period. - **Multiple testing:** when the predictor is one of many candidates, adjust (e.g., FDR / Bonferroni / data-mining-aware thresholds) and say so. Ignoring this is a known JFE red flag. ## Corporate finance - Define every variable with source, timing, and units; tabulate in a variable-definition table. - Winsorize/trim consistently and state the rule; show results are not winsorization artifacts. - Choose fixed effects deliberately (firm, industry-by-year, etc.) and justify what each absorbs. - **Standard errors:** cluster at the level of correlation in the residuals (often firm and/or time); use two-way clustering when both matter; match the cluster level to treatment assignment for causal designs. - Report economic magnitudes, not just significance — a coefficient is a number with units. ## Execution bridge (StatsPAI / Stata MCP) Run the asset-pricing battery, don't just specify it. Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). JFE is finance top-3 (with JF, RFS) — corporate-causal chain for corporate papers, factor-zoo haircut for asset pricing; attribute canon to the correct top-3 journal. - **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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md). ## Checklist - [ ] Every variable is defined with source, lag, and transformation - [ ] Winsorization/trimming rule stated and shown not to drive results - [ ] Sorting/breakpoint/weighting choices justified and stress-tested - [ ] Estimator matches the question (FM / GMM / panel FE) and is reported correctly - [ ] Standard errors clustered/corrected appropriately (often two-way) - [ ] Alphas reported against multiple benchmark models - [ ] Out-of-sample and multiple-testing discipline applied for predictors - [ ] Economic magnitudes interpreted, not just t-stats ## Anti-patterns - A new factor reported only in-sample, with no multiple-testing acknowledgment - Standard errors that ignore cross-sectional or time-series correlation - Breakpoints/weighting cherry-picked to maximize the spread - Fama–MacBeth without Newey–West or Shanken corrections - Variable definitions too vague to replicate - Reporting t-statistics while never stating the economic size of the effect ## Output format ``` 【Field】asset pricing | corporate finance 【Estimator】FM / GMM / panel FE / portfolio sort 【SE treatment】cluster dims / NW lags / Shanken 【Benchmarks】[models alphas are measured against] 【Inference discipline】out-of-sample? multiple-testing adjusted? 【Magnitudes stated】yes/no 【Next】jfe-robustness ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Financial-Economics-Skills/skills/jfe-empirical-design/SKILL.md`