Empirical R Research Code Auditor
LLMs generate syntactically clean R code that executes without errors while silently introducing catastrophic econometric biases. Compare naive prompt output against production academic econometrics.
Active Vulnerability Score:
4 / 4 Bugs Active
Observed Effect Est. (β)
-0.142
True Param: -0.018 (Null)
Standard Error & p-value
p = 0.004 ***
SE: 0.048 (Unclustered)
Observations (N)
600 (+140%)
True Unique N: 250
Specification Verdict
False Discovery
Unsound Academic Result
Empirical Pipeline Stages (Click to Inspect & Toggle Flaws)
Toggle individual fixes to observe real-time coefficient changes
1. Join & Merge
Many-to-Many Fanout
2. Panel Lags
Cross-Entity Lookahead
3. Missingness & Attrition
Selective Survivorship
4. Model & Clustering
Pooled OLS vs Clustered FE
Live Empirical Microdata Regression Fit
Naive Vibecoded Slope
Rigorous Econometric Ground Truth
Stage 1: Data Merging & Entity Key Integrity
Naive AI Prompt (ChatGPT / Copilot)
Syntax: Valid ✓
Production Academic Econometrics
Audited ✓
01
Why "Vibecoding" Fails in Panel Econometrics
When students ask an LLM "merge the minimum wage data with state unemployment and calculate a 1-year lag", the LLM will reliably output
df <- left_join(wages, unemp) %>% mutate(lag_wage = lag(wage)).
In R, this code runs flawlessly with zero errors or warnings. However:
- Cartesian Row Duplication: If the secondary dataset contains duplicate records per year,
left_join()duplicates rows silently. Standard errors collapse because $N$ is artificially multiplied. - Entity Boundary Leakage:
dplyr::lag()on an ungrouped tibble shifts values across entity boundaries (e.g., California year 2022 gets assigned Alabama's 2018 value). - Phantom Asterisks: Standard OLS
lm()assumes independent and identically distributed (i.i.d.) errors, ignoring serially correlated state shocks and inflating $t$-statistics by 200–400%.
Econometric Correction:
fixest::feols(log(employment) ~ min_wage + lag_wage | state_id + year, cluster = ~state_id, data = panel_df)
Reproducible R Markdown & Audit Log Export
Download clean R code with academic defensive practices and proof metrics for your research project.
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