PhD Researchers & Doctoral Candidates
A CGE, DSGE, or simulation model for a thesis—built transparently and stress-tested for uncertainty.
Research Methods
Economic Modelling & Simulation
Some questions can't be answered by looking at the past—what if this tax changed, this shock hit, this policy passed? We build structural and simulation models that let you ask them, tracing effects through an entire economy or system and reporting the uncertainty honestly, not as false precision.
CGE · DSGE · input-output · SAM
Microsimulation · ABM · system dynamics
Cost-benefit & welfare analysis
Reproducible, journal-ready
Sample output
policy scenario
baseline
Overview
Answering the questions data alone can't
Econometrics tells you what happened; economic modelling lets you ask what would happen. A carbon tax, a trade agreement, a demographic shift, a new benefit—none has a clean natural experiment, and their effects ripple through sectors, incomes, and behaviour in ways a single regression can't trace. Structural and simulation models are built to answer exactly these counterfactual, economy-wide questions.
We choose the framework the question requires. For medium-run policy effects across sectors, a CGE model calibrated to a social accounting matrix; for short-run macro dynamics and expectations, DSGE; for tracing a shock through supply chains, input-output analysis; for distributional effects, microsimulation on household data; for emergence and feedback, agent-based modelling or system dynamics. And because every model rests on assumptions, we make them explicit and stress them with Monte Carlo simulation and systematic sensitivity analysis.
Whether the deliverable is a policy-impact study, a welfare or cost-benefit analysis, or a scenario simulation, the discipline is the same: a transparent model, honest uncertainty, and reproducible files a reviewer or a decision-maker can interrogate.
Who We Work With
For counterfactual & impact questions
If you need to model what would happen—not just what did—this is the right desk to write to.
Policy Economists & Analysts
Policy-impact modelling of tax, trade, energy, and structural reforms across sectors and the economy.
Development & Environmental Researchers
Economy-wide and distributional analysis of climate, energy, and development interventions.
Health & Welfare Economists
Cost-effectiveness, cost-utility, and budget-impact analysis for health and welfare programs.
Research Institutes & Think Tanks
Scenario and impact modelling where the analysis informs public debate and policy design.
Government & International Agencies
Structural modelling and economic-impact assessment for evaluation and planning.
Capabilities
The full modelling & simulation toolkit
Organized across structural models, simulation methods, and economic-evaluation analysis. If your study needs a method not listed here, ask—this is the core, not the boundary.
Economy-wide frameworks
Calibrated, structural models that trace effects across sectors, markets, and the whole economy.
- CGE models
- DSGE models
- Input-output models
- Social accounting matrix
Dynamics, agents & uncertainty
Simulation approaches for heterogeneity, feedback, and uncertainty that closed-form models can't capture.
- Microsimulation
- Agent-based modelling
- System dynamics
- Monte Carlo simulation
- Numerical optimization
Impact, welfare & cost analysis
The applied evaluation methods that translate a model into a decision about value and impact.
- Economic impact modelling
- Welfare analysis
- Cost-benefit analysis
- Cost-effectiveness analysis
- Cost-utility analysis
- Budget-impact analysis
How the Analysis Works
Six steps from question to scenario results
A transparent sequence where the model's assumptions and its uncertainty are made explicit, not buried. Nothing is a black box.
Steps are adapted to your question: equilibrium vs. dynamic vs. agent-based, and single-scenario vs. full uncertainty analysis. We confirm the model and data with you before building.
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1
Frame
Define the counterfactual, the scope, and the outcomes—what policy or shock, over what horizon, affecting whom.
Inputs: counterfactual · scope · horizon · outcomes
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2
Select model
Choose the modelling framework the question demands and confirm the data it requires is available.
Models: CGE · DSGE · I-O · microsimulation · ABM
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3
Calibrate
Build and calibrate the model to a benchmark dataset—a SAM, I-O table, or micro-data—and validate the baseline.
Data: SAM · I-O table · micro-data · parameters
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4
Simulate
Run the policy or shock scenarios against the baseline and compute the outcomes of interest.
Output: scenarios · impacts · distributional effects
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5
Stress-test
Vary the uncertain parameters with Monte Carlo and sensitivity analysis to show how robust the results are.
Checks: Monte Carlo · sensitivity · alternative closures
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6
Report
Deliver scenario results, welfare or cost-benefit findings, figures, methodology, and reproducible model files you keep.
Output: results · figures · methods · model files
Rigor by default
The checks that make a model credible
A simulation is only as trustworthy as its assumptions and its uncertainty analysis. Making both explicit is standard on every engagement.
Included on every project
- Explicit statement of model assumptions and closures
- Baseline validation against observed data
- Monte Carlo and systematic sensitivity analysis
- Alternative-scenario and robustness checks
- Reproducible model files and calibration data you keep
What You Receive
Every engagement, delivered in full
Not a black-box result and a number, but a complete, documented package you can submit, defend, and reproduce.
- Documented model with stated assumptions
- Calibration data and validated baseline
- Scenario results and impact estimates
- Distributional and welfare effects, where relevant
- Monte Carlo and sensitivity analysis
- Cost-benefit or economic-evaluation findings
- Interpretation and policy implications
- Reproducible GAMS, Dynare, R, or Python model files
- Journal-ready methodology and results sections
Where this fits
Part of a larger arc
Economic modelling is strongest when the data and estimation ahead of it are sound and the reporting after it is precise—each handled with the same care.
Research Design & Planning
Identification strategy, power, and specification decided before estimation begins.
Explore methodsStatistical & Methodological Audit
An independent check of assumptions, specification, and reproducibility before submission.
Explore auditPublication & Research Support
Methods and results reporting, journal selection, and reviewer-response support.
Explore support
FAQ
Common questions
Answers to what most researchers and project leads ask before we begin an economic-modelling or simulation engagement.
Both are structural, economy-wide models but answer different questions. CGE models focus on the medium-to-long-run effects of a policy or shock across sectors and markets—trade, tax, and structural change—using a calibrated dataset. DSGE models emphasize short-run dynamics, expectations, and business cycles, and are common in macro and monetary analysis. We recommend the framework your question actually calls for.
Yes—that is economic impact modelling, and depending on the question we use input-output analysis, a social accounting matrix, or a CGE model to trace how a change ripples through sectors, incomes, and the wider economy, capturing indirect and induced effects a partial analysis would miss.
When the system's behaviour emerges from interactions and feedback rather than equilibrium. Agent-based models simulate heterogeneous actors following rules, useful when heterogeneity and emergence matter; system dynamics captures stocks, flows, and feedback loops over time. Both suit questions where the aggregate is more than the sum of average behaviour.
Yes. We conduct cost-benefit, cost-effectiveness, cost-utility, and budget-impact analyses to the standards expected in policy and health economics—discounting, appropriate outcome measures (such as QALYs where relevant), and sensitivity analysis on the key assumptions.
Through Monte Carlo simulation and systematic sensitivity analysis. Rather than reporting a single deterministic result, we vary the uncertain inputs across plausible ranges to produce distributions of outcomes, so the range of possibilities—and which assumptions drive them—is explicit.
It depends on the model. CGE and input-output models are built on a benchmark dataset (a SAM or I-O table) and calibrated rather than estimated; microsimulation needs micro-data; DSGE combines calibration with estimation. We assess data feasibility up front and are candid if the question outruns the available data.
GAMS and GEMPACK for CGE, Dynare (MATLAB) for DSGE, R and Python for input-output, microsimulation, agent-based, and Monte Carlo work, and specialized packages as needed. You receive the model files, calibration data, and a methods section written to journal standards.
Yes, and it is the best time. The choice of model, the data it requires, and the assumptions it rests on all shape what the analysis can credibly conclude—settling them at the outset prevents building an ambitious model the data or the question cannot support.
Related Expertise
Often paired with econometrics
Modelling a policy or a shock?
Tell us the counterfactual and the outcomes you care about—we'll recommend the right model, build it transparently, and report the uncertainty honestly.