pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 18-jusi-aalto-stata-accounting-research --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 18-jusi-aalto-stata-accounting-research -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 18-jusi-aalto-stata-accounting-research -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 18-jusi-aalto-stata-accounting-research -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 18-jusi-aalto-stata-accounting-research -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 18-jusi-aalto-stata-accounting-research -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 18-jusi-aalto-stata-accounting-research -a windsurf -y
name: stata-accounting-research description: | STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me code for [technique]" — including: entropy balancing, propensity score matching (PSM), difference-in-differences (DiD), regression discontinuity (RDD), instrumental variables (IV), event studies (CAR/BHAR), survival analysis, Fama-MacBeth regressions, bootstrap, quantile regression, reghdfe/xtreg/areg, clustering standard errors, fixed effects, esttab/outreg2 table formatting, winsorization, leads/lags. Users can specify their variables (e.g., treatment, outcomes, controls) and receive adapted syntax. NOTE: This skill provides code patterns from published papers, not research design advice.
This skill is a **code pattern library**, not a methodological advisor.
| Can Do | Cannot Do | |--------|-----------| | Show *how* published papers implemented methods | Explain *when* to use one method over another | | Provide tested STATA syntax | Advise on identification strategy | | Indicate which robustness tests accompany analyses | Discuss research design trade-offs | | Cite source papers for code patterns | Recommend optimal research design |
**When users ask methodology questions** (e.g., "Should I use entropy balancing or PSM?", "How do I address endogeneity?", "Is my identification strategy valid?"):
1. Acknowledge the limitation: "This skill provides code patterns from published papers, not research design guidance." 2. Show how different papers approached similar problems (code examples) 3. Suggest consulting methodology references: Breuer & deHaan (2024) for fixed effects, Angrist & Pischke for causal inference, or the user's methodologist/advisor 4. Offer to show multiple implementations so the user can see variation in approaches
Use `references/REFERENCES.md` as the primary index, then read targeted .do files.
Search `references/REFERENCES.md` to identify relevant papers. The index contains structured metadata:
Example queries on REFERENCES.md:
Read only the identified .do files to extract actual syntax. This reduces context usage and improves accuracy.
1. Adapt patterns to the user's variable names and research context 2. Cite source: "Based on [Authors] ([Year]), JAR [Volume]([Issue])"
For very specific syntax queries (e.g., "how does absorb() handle singletons?"), grep .do files directly:
| Task | Grep Pattern | |------|--------------| | Panel regressions | `reghdfe\|xtreg\|areg` | | Fixed effects | `absorb\(\|i\.year\|i\.firm` | | Clustering | `cluster\(\|vce\(cluster` | | Matching/PSM | `psmatch2\|teffects\|cem\|ebalance\|pscore` | | IV regression | `xtivreg\|ivregress\|ivreg2` | | DiD | `post.*treat\|treat.*post\|parallel.*trend` | | RDD | `rdrobust\|rddensity` | | Event studies | `CAR\|BHAR\|abnormal.*return` | | Survival | `stcox\|streg\|stset` | | Fama-MacBeth | `fama.?macbeth\|newey.*west` | | Bootstrap | `bootstrap\|bsample` | | Quantile regression | `qreg\|sqreg\|bsqreg` | | Table output | `esttab\|outreg2\|eststo` | | Winsorization | `winsor\|winsor2` |
126 STATA .do files from JAR Volumes 55-63 (2017-2025). See `references/REFERENCES.md` for complete catalog with paper titles and authors.
| Volume | Year | Papers | |--------|------|--------| | 55 | 2017 | 9 | | 56 | 2018 | 12 | | 57 | 2019 | 9 | | 58 | 2020 | 13 | | 59 | 2021 | 4 | | 60 | 2022 | 22 | | 61 | 2023 | 22 | | 62 | 2024 | 25 | | 63 | 2025 | 10 |
* Firm and year FE with firm-clustered SEs (most common) reghdfe depvar indepvar controls, absorb(firm year) cluster(firm) * Industry-year FE reghdfe depvar indepvar controls, absorb(ind_year) cluster(firm)
eststo clear eststo: reghdfe depvar indepvar controls, absorb(firm year) cluster(firm) esttab using "table.tex", replace star(* 0.10 ** 0.05 *** 0.01) se
winsor2 varlist, cuts(1 99) replace
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