stata-accounting-research
SkillFiles & storageLets your agent find and adapt STATA code examples for accounting research methods like matching, DiD, and event studies.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the stata-accounting-research skill
About this capability
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]" —
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/18-jusi-aalto-stata-accounting-research/SKILL.md and read by ahel’s review.
Scope and Limitations
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?"):
- Acknowledge the limitation: "This skill provides code patterns from published papers, not research design guidance."
- Show how different papers approached similar problems (code examples)
- Suggest consulting methodology references: Breuer & deHaan (2024) for fixed effects, Angrist & Pischke for causal inference, or the user's methodologist/advisor
- Offer to show multiple implementations so the user can see variation in approaches
Workflow
Use references/REFERENCES.md as the primary index, then read targeted .do files.
Stage 1: Index Search
Search references/REFERENCES.md to identify relevant papers. The index contains structured metadata:
- Primary Method: STATA commands used (reghdfe, psmatch2, stcox, etc.)
- Identification Strategy: DiD, PSM, IV, RDD, Event Study, etc.
- Robustness/Special Features: Winsorization levels, clustering specs, placebo tests, etc.
Example queries on REFERENCES.md:
- "entropy balancing" → finds JAR_60_alv, JAR_60_bl, JAR_61_ds, JAR_62_5_llz, JAR_63_2_npstv
- "stacked DiD" → finds JAR_61_ds, JAR_62_5_aov, JAR_62_5_gibbons
- "Cox hazard" → finds JAR_59_ctv, JAR_62_2_xyz
Stage 2: Code Extraction
Read only the identified .do files to extract actual syntax. This reduces context usage and improves accuracy.
Stage 3: Adaptation and Citation
- Adapt patterns to the user's variable names and research context
- Cite source: "Based on [Authors] ([Year]), JAR Volume"
Fallback: Direct Grep Patterns
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 |
Corpus Overview
126 STATA .do files from JAR Volumes 55-63 (2017-2025). See references/REFERENCES.md for complete catalog with paper titles and authors.
File Naming Convention
- V55-61:
JAR_{volume}_{shortcode}.do - V62-63:
JAR_{volume}_{issue}_{shortcode}_{authors}.do
Volume Coverage
| 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 |
Standard Patterns
Clustering and Fixed Effects
* 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)
Output Conventions
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
Winsorization
winsor2 varlist, cuts(1 99) replace
Signals
- GitHub stars
- 4k
- Forks
- 476
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
stata-accounting-research- Source
- github.com/brycewang-stanford/auto-empirical-research-skills