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Checklist of empirical robustness tests for finance/economics papers
Passed 19 of 22 test cases with the skill, and 18 of 22 without it.
1 fixed18 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
I'm running an instrumental variables regression to address endogeneity concerns regarding executive network centrality and M&A performance. What specific identification tests, weak instrument diagnostics, and alternative specification checks should I run and report to ensure my empirical strategy passes rigorous referee scrutiny?
| Checked | Without skill | With skill |
|---|---|---|
| The response explicitly recommends reporting reduced form results showing the direct regression of outcome on the instrument. | Missed | Passed |
| The response explicitly instructs comparing OLS vs IV coefficient magnitudes. | Missed | Passed |
| The response specifies checking first-stage F-statistic exceeding at least 10. | Passed | Passed |
The instructions your model reads when the skill loads.
Systematic checklist of robustness tests for empirical research. Use this to ensure comprehensive testing before submission.
| Test | Description | When to Use |
|---|---|---|
| Exclude outliers | Winsorize/trim at different levels (0.5%, 2%, 5%) | Always |
| Drop financial firms | Exclude SIC 6000-6999 | If not already excluded |
| Drop regulated industries | Exclude utilities, telecoms | Industry-specific effects |
| Different time periods | Split sample pre/post crisis, early/late | Results may be period-specific |
| Geographic subsamples | By region, state, country | External validity |
| Size subsamples | Small vs. large firms | Heterogeneous effects |
| Balanced panel | Require continuous observations | Survivorship concerns |
| Test | Description | When to Use |
|---|---|---|
| Different fixed effects | Firm, industry×year, state×year | Control for unobservables |
| Additional controls | Add variables referees might suggest | Omitted variable concerns |
| Drop controls | Verify not over-controlling | Mediator concerns |
| Different clustering | Firm, industry, state, two-way | Inference robustness |
| Different standard errors | Bootstrap, Newey-West, Driscoll-Kraay | Serial/cross-sectional correlation |
| Nonlinear specifications | Quadratic terms, splines | Linearity assumption |
| Log vs. level | Transform dependent variable | Skewed distributions |
| Test | Description | When to Use |
|---|---|---|
| Alternative dependent variable | Different proxy for same concept | Measurement concerns |
| Alternative treatment measure | Continuous vs. binary, different threshold | Treatment definition |
| Alternative control measures | Different proxies for size, leverage, etc. | Standard practice |
| Scaled differently | By assets, sales, employees | Scaling choice matters |
test cases passed, vs 18 / 22 without
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