rje-data-analysis

name	rje-data-analysis
description	Use when executing and stress-testing the empirical analysis for a RAND Journal of Economics (RJE) industrial-organization manuscript — estimating structural demand/supply, entry, auction, or reduced-form models, then running the robustness, counterfactual, and inference checks IO referees expect. Analysis discipline, not study design.

Data Analysis & Robustness (rje-data-analysis)

When to trigger

• Estimates are in hand and you need the robustness suite IO referees demand
• A structural counterfactual needs validation before it goes in the article
• You must report inference correctly for market-level / clustered data

Analysis norms at the IO flagship

RJE referees apply industrial-organization empirical norms. Whether the work is structural or reduced-form, the analysis must show the estimates are credible, well-behaved, and economically sensible, and that counterfactuals are disciplined by the model.

Structural work

• Estimation diagnostics: report objective-function value, convergence, and sensitivity to starting values (non-convex objectives); use multiple starts and report them.
• Economic sanity: elasticities, markups, and marginal costs in plausible ranges; own-price elasticities negative and large enough for positive markups.
• Identification-in-practice: show which moments move which parameters (sensitivity / informativeness of moments).
• Counterfactuals: state maintained assumptions (e.g., fixed product set, conduct unchanged), report them as ranges/bounds, and validate against any out-of-sample episode (a known merger, entry, or price change).

Reduced-form work

• Modern DID where timing is staggered (Callaway–Sant'Anna / Sun–Abraham), with event-study leads and a Goodman-Bacon decomposition.
• Weak-IV-robust inference where instruments are weak; report the first-stage F.
• Placebo / falsification on markets or periods that should not respond.

Inference (both)

• Cluster at the level of treatment/market variation; with few clusters use wild-cluster bootstrap.
• Set and report seeds for simulation, bootstrap, and randomization.

Robustness suite to stage

• Alternative demand specification / functional form (structural) or alternative controls and sample (reduced-form)
• Alternative instruments and conduct assumptions
• Subsample and alternative market-definition checks
• Sensitivity of the headline counterfactual / welfare number to key assumptions

Page-cap discipline

RJE's caps are hard (main text <=40 pp, total <=50 pp). Put the core estimates and one or two decisive robustness exhibits in the main text; move the full robustness battery to the appendix (within the <=10-page appendix+references budget), not into discouraged supporting information.

Diagnostic triage table (structural estimates that IO referees flag)

When a structural estimate looks off, diagnose the symptom first.

Symptom	Likely cause	First check to stage
Positive own-price elasticity	Price endogeneity unhandled or weak instruments	First-stage strength of cost shifters / BLP instruments
Implausibly large markups (>60%)	Conduct misspecified or marginal cost too low	Re-estimate under alternative conduct; inspect cost FOCs
Estimates jump across starting values	Non-convex GMM objective, flat ridges	Multi-start grid; report objective at each start
Counterfactual price swings wildly	Extrapolation outside observed variation	Bound the counterfactual; restrict to in-support changes
Substitution ignores obvious rivals	Too few random coefficients / no micro-moments	Add micro-moments or a nesting structure

Worked vignette: validating a merger simulation

Suppose you estimate random-coefficients logit demand for ready-to-eat cereal, recover marginal costs from Bertrand-Nash FOCs, and simulate a two-brand merger (illustratively, median markup 35%, predicted price rise 4.2% for the merging brands).

• Economic sanity: own-price elasticities near -3.5 are plausible for branded cereal; report that 35% markups sit within the literature's range.
• Identification-in-practice: show cost-shifter instruments move the price coefficient and differentiation instruments move the random-coefficient variances.
• Counterfactual discipline: hold product set and conduct fixed, state it, and report the rise as a range (3.1%-5.4%) across specifications.
• Validation: if a comparable merger occurred nearby, check whether the model predicts its realized price path.

A bare "+4.2%" with no band and no validation invites the first referee pushback below.

Referee-pushback patterns and the venue fix

• "Your counterfactual extrapolates outside observed price variation." Fix: restrict the simulated change to the support of observed prices, or report bounds and flag the extrapolation explicitly.
• "Markups are mechanical artifacts of the conduct assumption." Fix: test conduct where the data allow, or show the markup ranking is robust across Bertrand, Cournot, and partial-collusion assumptions.
• "Inference ignores within-market correlation." Fix: cluster at the market level; with few markets, switch to wild-cluster bootstrap and report the seed.
• "Robustness lives only in a footnote." Fix: stage one decisive robustness exhibit in the main text and route the full battery to the appendix.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. RAND is industrial organization — endogeneity of prices/entry and structural demand; the reduced-form chain for causal claims, structural IO outside it.

• 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.

Anti-patterns

• A single structural run with no starting-value or specification sensitivity
• Counterfactuals reported as point estimates with no assumption bounds
• TWFE on staggered policy timing presented as the headline
• Default robust SEs when variation is at the market level

Output format

【Estimator】structural (demand/conduct/entry/auction) / reduced-form
【Economic sanity】elasticities/markups plausible? [Y/N]
【Robustness done】[specifications, instruments, conduct, subsamples]
【Counterfactual】assumptions stated + bounded? [Y/N]
【Inference】clustering / weak-IV / seeds set? [Y/N]
【Page budget】main robustness in appendix? [Y/N]
【Next step】rje-tables-figures