Economic Modelling Submission Guide
A source-checked guide to deciding whether a theoretical or applied economics model is ready for Economic Modelling.
Readiness scan
Find out if this manuscript is ready to submit.
Run the Free Readiness Scan before you submit. Catch the issues editors reject on first read.
Quick answer: Submit to Elsevier's Economic Modelling when the model changes an economic conclusion, clarifies a policy tradeoff, or improves empirical interpretation, and its assumptions, discipline, benchmarks, and sensitivity make that change inspectable. A technically larger model is not automatically a stronger contribution.
Evidence basis: We reviewed the official journal page, guide for authors, and Editorial Manager route on August 29, 2026. Publisher facts are sourced; the readiness framework below is Manusights judgment.
Identify what the model changes
Model contribution | Evidence readers need | Weak-contribution signal |
|---|---|---|
Structural policy model | Identification/calibration discipline, fit, mechanism, counterfactual, and uncertainty | More sectors or frictions are added without changing a decision |
Econometric model | Estimand, specification logic, diagnostics, benchmarks, and out-of-sample behavior | Fit improves while interpretation becomes less stable |
Forecasting model | Pseudo-out-of-sample design, real-time information set, strong baselines, and revisions | The comparison uses revised data unavailable to a forecaster |
Theoretical model | Economic puzzle, transparent assumptions, mechanism, and comparative result | Complexity is the main novelty |
The official scope covers theoretical and applied modeling across macroeconomics, development, energy, environment, finance, health, industry, international, labor, micro, public, and urban economics. The cross-field breadth makes the modeling contribution, not merely the topic, the canonical reader job.
Build an assumption-to-result ledger
For every headline result, note the assumption or data moment that carries it, the nearest alternative specification, and the decision that changes if the result holds.
Audit | Strong evidence | Failure boundary |
|---|---|---|
Discipline | Parameters tied to data, literature, or identified moments | Convenient values drive the result |
Comparison | Nested or established alternatives under matched conditions | The baseline is intentionally incomplete |
Sensitivity | Economically plausible ranges and joint uncertainty | One-at-a-time checks miss interacting assumptions |
Interpretation | Mechanism traced from input to outcome | A black-box response is labeled causal |
Worked example: a carbon-tax macro model
A draft adds an energy block and reports output losses from a carbon tax. A stronger manuscript shows which substitution elasticity drives the response, matches energy and emissions moments, separates revenue-recycling designs, compares transitional and long-run effects, and reports distributional or sectoral boundaries. The useful contribution is not that the model contains carbon; it is that the model changes a policy tradeoff transparently.
Three failure patterns in the model audit
In our editorial analysis of the current Economic Modelling scope and guide, we map the paper's claimed decision value to assumptions, data discipline, benchmark results, and sensitivity. This source-backed synthesis does not use private journal information and cannot predict acceptance.
The guide cannot determine an editorial outcome. It checks whether the model description, equations, parameter or estimation table, code and data route, benchmark results, sensitivity analysis, and conclusion form a reproducible chain. We pay special attention to assumptions that are described as standard but materially control the result. Those assumptions should be named beside the decision they influence, not hidden in an appendix after the conclusion has generalized beyond them.
Economic Modelling size-as-contribution pattern. The methods add sectors, agents, states, or equations, but the results do not change a mechanism, empirical interpretation, or policy choice. Name the smallest model feature that changes the conclusion and compare against a nested version without it.
Economic Modelling convenient-calibration pattern. The main result depends on a parameter selected for fit or precedent without showing plausible uncertainty. Link parameters to data or literature, test joint ranges, and identify the value at which the conclusion reverses.
Economic Modelling information-set mismatch pattern. A forecast or policy simulation uses revised data, future information, or tuning access unavailable at the decision date. Reconstruct the information set, data vintage, and benchmark protocol that a real user would have faced.
Finally, trace every headline result from the abstract to its table or figure, then through the equation, code, data, or assumption that generates it. If the chain breaks, the paper needs a reproducibility or interpretation repair before submission.
Readiness check
Run the scan against the requirements while they're in front of you.
See score, top issues, and journal-fit signals before you submit.
Assemble a model-reproduction package
Confirm exact live fields before upload. The following working artifacts should agree before the system generates a review PDF.
As applicable to the model and article type, reconcile the cover letter, data availability statement, ethics statement, conflicts of interest, author contributions, funding statement, supplementary files, and highlights or graphical abstract. The guide reviewed here gives no fixed main-text word limit and no fixed initial figure limit across all covered article types; verify article-specific instructions before upload.
Required working artifact | What it makes testable | Hold signal |
|---|---|---|
Anonymized manuscript | Model question, equations, discipline, results, and limits without avoidable identity leakage | Names, acknowledgments, metadata, or identifying repository links remain |
Equation and assumption ledger | Definitions, closures, timing, equilibrium conditions, priors, and restrictions | A result-driving assumption is called standard but never stated or tested |
Reproduction bundle | Code version, data vintage, parameters, estimation or solution settings, and output commands | The main table cannot be regenerated from archived inputs |
Benchmark and sensitivity matrix | Nested alternatives, matched information, plausible joint ranges, and reversal points | Only one-at-a-time sensitivity or a weak baseline is reported |
Data and disclosure statements | Access conditions, funding, conflicts, contributions, and reuse boundaries | The statement promises data or code a reader cannot locate or lawfully use |
Read these artifacts as a causal chain. A policy result depends on a model response; the response depends on equations, parameters, expectations, and data discipline; and credibility depends on comparisons and uncertainty. If the conclusion survives only because one layer is opaque, the package is not ready.
Editorial process stage map
The official guide describes initial assessment and double-anonymized peer review. It does not guarantee an individual timetable, so use this as a preparation map.
- Stage 1, portal intake: prove completeness, anonymity, classifications, and declarations.
- Stage 2, initial editorial assessment: expose modeling ownership, contribution, and minimum evidence.
- Stage 3, external review if invited: make discipline, benchmarks, sensitivity, and reproduction auditable.
- Stage 4, revision or decision: tie each response to a changed model artifact or bounded claim.
Stage | Decision boundary | Author-side readiness check |
|---|---|---|
Portal intake | Complete files, anonymity, classifications, and declarations | Review PDF, title page, highlights, data statement, and supplements agree |
Initial editorial assessment | Modeling ownership, contribution, policy relevance, and minimum evidence | The abstract names what the model changes and the first result shows why it matters |
External review if invited | Assumptions, discipline, solution or estimation, benchmarks, sensitivity, and reproducibility | A reader can trace the headline result to inputs and identify where it reverses |
Revision or decision | Whether concerns are resolved without shifting the central claim | Every response points to a changed artifact or gives a transparent technical reason |
Separate the journal from nearby owners
Venue direction | Best for | Stronger fit when | Reroute signal |
|---|---|---|---|
Economic Modelling | Change an economic conclusion through model construction, estimation, or validation | The model and its discipline are central contributions | Modeling is merely a vehicle for a field-specific result |
Journal of Macroeconomics | Advance a macroeconomic mechanism, policy, fluctuation, or forecast | The macro question dominates and the model supports it | The contribution is about modeling across fields |
Computational Economics | Advance computational methods and applications in economics | Algorithms, numerical methods, or implementation are central | The economic decision matters more than computation |
Applied Economics | Deliver an applied economic finding across a broad topic range | The empirical result is primary and the model is standard | The paper claims a model contribution without methodological evidence |
This is an intent-boundary tool, not a quality ranking. The right owner is the venue whose readers need the paper's main reusable contribution, subject to its current official scope.
Prepare the current package
- Confirm that modeling is the contribution rather than a presentation device.
- Reproduce headline tables and figures from the final code, data, and parameter files.
- Reconcile abstract, equations, calibration/estimation, sensitivity, appendices, and conclusion.
- Prepare the double-anonymized review file and separate identifying material required by the live guide.
- Recheck highlights, graphical abstract, data statements, disclosures, fees, and current submission policies.
- Inspect the generated PDF and submit through the official route.
Submit if
- Readers can name the economic decision the model changes.
- Assumptions and empirical discipline are traceable to the result.
- Comparisons use credible alternatives under matched information.
- Sensitivity and limitations preserve the claim's economic boundary.
Think twice if
- The Methods section uses model size as a substitute for a new mechanism or conclusion.
- The main results table depends on a convenient parameter with no discipline or sensitivity boundary.
- Forecast evaluation uses information that would not have been available in real time.
- A field journal owns the substantive reader job more clearly than a modeling journal.
Run the final Economic Modelling readiness review.
Official sources accessed August 29, 2026.
- Economic Modelling, Elsevier.
- Guide for authors, Elsevier.
- Official submission route, Editorial Manager.
Frequently asked questions
The official scope welcomes theoretical and applied papers in macroeconomics and other economics fields when economic modeling is central and policy-relevant.
The current official guide describes double-anonymized peer review after an initial editorial assessment.
The official journal page currently routes submissions to the Economic Modelling Editorial Manager site.
No. It organizes public instructions and model-readiness checks but cannot predict a decision.
Before you upload
Choose the next useful decision step first.
Move from this article into the next decision-support step. The scan works best once the journal and submission plan are clearer.
Use the scan once the manuscript and target journal are concrete enough to evaluate.
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