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name: qe-topic-selection description: Use when first judging whether a project fits Quantitative Economics (QE) — a substantive economic question answered with serious quantitative methods (empirical, structural/computational, experimental, or simulation), sister to Econometrica and Theoretical Economics. Tests fit and sharpens the question; it does not design the estimation.

Topic Selection (qe-topic-selection)

When to trigger

• You have data, a model, or an experiment but are unsure QE is the right home
• The project feels like "pure theory" or "pure method" and you suspect a sibling journal fits better
• The economic question behind the quantitative exercise is not yet sharp
• You are choosing between QE, Econometrica, and a top field journal

The QE fit bar

QE is the Econometric Society's general-interest, empirically and quantitatively oriented journal. Its comparative advantage among the ES trio is explicit: Econometrica leans theoretical/methodological, Theoretical Economics is pure theory, and QE publishes papers that develop or apply quantitative methods to substantive economic questions — empirical, computational/structural, experimental, and simulation-based — with a strong premium on documented data and reproducible code. The fit test is two-pronged:

1. Is there a first-order economic question? A quantitative exercise with no economic payoff (an estimator with no application, a simulation with no question) drifts toward Econometrica or a methods outlet.
2. Does answering it require serious quantitative work? A purely descriptive note without methodological or computational content under-fits QE's quantitative identity.

The sweet spot is a paper where the method and the answer reinforce each other: a structural model that delivers a counterfactual a reduced-form design cannot; an empirical design that pins down a parameter the literature has only assumed; an experiment whose data discipline a quantitative model; a simulation that resolves a measurement puzzle.

Paper archetypes that fit QE

• Structural / computational: estimate a model, then run a policy counterfactual or welfare calculation.
• Applied micro / finance with quantitative ambition: a credible causal design whose magnitudes feed an economic quantity of interest.
• Experimental: lab / lab-in-the-field / online experiments whose data identify a parameter or test a quantitative theory (note QE's Jan 2026 pre-registration and instructions rules).
• Simulation-based / measurement: new data or methods that quantify something previously unmeasured, with reproducible code.

Checklist

• The substantive economic question is stated in one sentence a non-specialist cares about
• The quantitative method is necessary to answer it (not decoration, not the whole point)
• The contribution is general-interest, not confined to one narrow subfield
• Data and code can be documented and made non-exclusive (ES policy) — no fatal access barrier
• QE beats the sibling alternatives: not pure theory (TE), not method-first (Econometrica)
• If experimental/own-data: a recognized pre-registration is feasible (effective Jan 2026)

Anti-patterns

• A new estimator with a toy application — likely Econometrica, not QE
• A pure theorem with no quantification — Theoretical Economics
• A descriptive note with no quantitative or methodological content
• A question so narrow that only one subfield would cite the answer
• Data so locked down that the ES reproducibility regime cannot be satisfied

Routing a project across the Econometric Society trio

The three ES journals are open-access siblings; the fit test routes a project among them by what the contribution primarily is.

Project shape	Best ES home	Tell
theory-meets-data-meets-computation; a quantitative answer	Quantitative Economics	a number the field lacked + reproducible code
a new estimator or limit theorem, application secondary	Econometrica	the method is the point
a model and proofs, no quantification	Theoretical Economics	no estimand, no data

When two homes seem plausible, ask which sentence the abstract would lead with — a quantity (QE) or a theorem/estimator (Econometrica/TE).

Worked vignette: a fit judgment in practice (illustrative)

A team has panel data and a new control-function estimator for a production function with unobserved productivity. If the paper's punchline is "our estimator has better finite-sample properties," the home is Econometrica. The QE pivot: use the estimator to answer a question — "correcting the bias raises the estimated returns to scale from 0.92 to 1.04 (illustrative), overturning the constant-returns benchmark for this industry." Now the method serves a quantitative answer with a reproducible package, and the fit is QE rather than a methods outlet.

Output format

【Question】one sentence, general-interest?
【Quantitative method】structural / empirical / experimental / simulation
【Why the method is necessary】...
【Sibling check】not pure theory (TE), not method-first (Econometrica)? [Y/N]
【Reproducibility feasible】data/code can be documented + non-exclusive? [Y/N]
【Next step】qe-literature-positioning