Skills Data Science Econometric Robustness Strategy Guide

Econometric Robustness Strategy Guide

v20260724
ecopol-robustness
A comprehensive guide for structuring and communicating robustness checks in economic policy papers. It teaches authors how to organize tests not as a checklist of alternative specifications, but by the specific threat (e.g., sample size, functional form, pre-trends) they neutralize. The focus must remain on defending the policy-relevant magnitude and confidence interval in the main text, preventing the paper from becoming a mere appendix dump.
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Overview

Robustness Strategy (ecopol-robustness)

When to trigger

  • The headline policy magnitude moves under plausible alternative specifications
  • A discussant could attribute the result to one sample, period, or country
  • Inference rests on few clusters / few treated units (common in policy evaluations)
  • The result is statistically significant but the magnitude — the part a policymaker uses — is imprecise
  • You are pre-empting the academic discussant's "have you tried…" before the conference

Robustness is discussant-proofing, not a checklist dump

At EP, robustness has a specific purpose: the paper will be debated live by two invited discussants who act as the referees, and the academic one will arrive with a list of fragility tests. So organize robustness by the threat each test neutralizes, not as a mechanical appendix of "alternative specifications." A reader (and the policy discussant) should see, for each test, which way of being wrong it rules out. Because EP papers carry a recommendation, the central object to defend is the magnitude and its confidence interval, not just the sign.

The threat-to-test map (build the robustness section from this)

Threat a discussant will raise The test that answers it
"It's driven by one period/sample" leave-one-out by year/region; pre/post-crisis split
"Functional form is doing the work" alternative specifications; semi/non-parametric check
"Parallel trends / pre-trends fail" event-study leads; honest-DID / pre-trends sensitivity bounds
"Confounders you didn't control for" coefficient-stability / Oster δ bounds; placebo on unaffected groups
"Inference is too optimistic with few clusters" wild-cluster bootstrap; randomization inference
"Measurement error in the policy variable" alternative coding; instrument or bound the error
"It won't generalize beyond this case" replicate on a second jurisdiction/episode if data allow

Craft moves

  • Put a robustness summary in the main text, not buried 40 pages deep: a compact figure or sentence showing the headline number is stable across the threats that matter. The policy discussant will not read the appendix.
  • Distinguish robustness from heterogeneity. Stable-everywhere = robust; differs-by-group = a finding to report, not a failure to hide.
  • Report the range, not just a star. "The effect lies between X and Y across all specifications" is the EP-useful statement.
  • Pre-register the discussant's likely objections (use ecopol-referee-strategy) and answer the top three in the main text proactively — you cannot reply live as easily as in a referee letter, so anticipate.
  • Honesty about fragility. If one test moves the number, say what it implies for the policy conclusion rather than hiding it — discussants will find it.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. Economic Policy is policy-facing applied economics; foreground a credible design and a policy-relevant magnitude.

  • Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg.
  • OVB sensitivity: oster_delta / sensemakr.
  • Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley.
  • Re-fit off one handle: audit_result(result_id) lists missing checks + the exact suggest_function for each.
  • Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.

Checklist

  • Robustness section organized by threat, each test labeled with the threat it neutralizes
  • A main-text robustness summary (figure or sentence) — not appendix-only
  • Headline magnitude + CI shown to be stable (or its instability honestly bounded)
  • Few-cluster / few-treated inference addressed (wild bootstrap / randomization inference)
  • Pre-trends / parallel-trends sensitivity reported where the design needs it
  • Coefficient-stability or placebo evidence against unobserved confounders
  • Heterogeneity reported as a finding, not disguised as robustness
  • Top three likely discussant objections answered proactively in the main text

Anti-patterns

  • A 20-table appendix of "alternative specifications" with no map to the threats they address
  • Defending only significance while the policy-relevant magnitude swings across specifications
  • Standard clustered SEs with five treated jurisdictions and no few-cluster correction
  • Burying every robustness result in the appendix where the policy discussant never sees it
  • Quietly dropping a specification that breaks the result instead of reporting and interpreting it

Output format

【Journal】Economic Policy (EP)
【Skill】ecopol-robustness
【Headline magnitude】X (CI: [.,.]) — stable across threats? Y/N
【Threats neutralized】[sample / functional form / pre-trends / confounders / inference / measurement / external]
【Main-text summary】figure/sentence present? Y/N
【Few-cluster inference】method used
【Honest fragility note】what moves the number and the policy implication
【Next skill】ecopol-tables-figures
Info
Category Data Science
Name ecopol-robustness
Version v20260724
Size 5.67KB
Updated At 2026-07-28
Language