stata-accounting-research

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.

Scope and Limitations

This skill is a code pattern library, not a methodological advisor.

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

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

1. Adapt patterns to the user's variable names and research context
2. 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:

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

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