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Modeling

This guide outlines a typical modeling workflow in JCGE, from data-backed calibration to a solved model and exported outputs. It focuses on the structural steps and how the pieces fit together.

If you are new to the ecosystem, start with Getting Started and then return here for a deeper walkthrough of model construction.

1. Start from data

Use JCGECalibrate to load a SAM and produce calibrated parameters and starting values. The canonical input format is described in the Calibration and Imports guides.

Key ideas:

  • Data should be mapped into the canonical schema before building blocks.
  • Calibration outputs become model parameters and initial values.
  • Keep data transformations explicit so the pipeline is reproducible.

2. Build a RunSpec

A RunSpec is the structural description of a model: sets, mappings, sections, blocks, and closures. It is a readable definition of the economic structure separate from any specific calibration dataset.

using JCGECore
using JCGEBlocks

sets = Sets(goods, activities, factors, institutions)
mappings = Mappings(Dict(a => a for a in activities))

prod = production(:prod, activities, factors, goods; form=:cd, params=prod_params)
market = composite_market_clearing(:market, goods, activities)
numeraire = numeraire(:numeraire, :factor, :LAB, 1.0)

sections = [
    section(:production, [prod]),
    section(:markets, [market]),
    section(:closure, [numeraire]),
]

spec = build_spec("MyModel", sets, mappings, sections)

Common block types

JCGEBlocks provides a library of reusable economic blocks. Typical models mix:

  • Production blocks (Cobb-Douglas, CES, Leontief).
  • Market clearing blocks.
  • Demand or institutional behavior blocks.
  • Closure and numeraire blocks.

The Blocks guide gives concrete examples and parameterization details.

Closures and normalization

Closures specify which variables are fixed and which adjust. A numeraire normalizes the price level and avoids indeterminacy.

3. Validate

using JCGERuntime
report = validate_model(spec)
report.ok || error("Model validation failed")

Validation checks block consistency, coverage of sets, and structural integrity before solving. It is a good place to catch missing mappings or parameters.

4. Solve

using JCGERuntime, Ipopt
result = run!(spec; optimizer=Ipopt.Optimizer)

Solver configuration depends on the model class. For MCP models, PATHSolver is required. Use the Running guide for configuration patterns and runtime options.

5. Inspect and export

Use JCGEOutput to render equations or export results.

Typical outputs include:

  • Equation listings for verification.
  • Report tables by sector or account.
  • Scenario comparison exports.

Tips for building models

  • Start from a minimal model and expand iteratively.
  • Keep data and structure separate so you can swap datasets.
  • Prefer reusable blocks over copy-pasted equations.
  • Make closures explicit and document them in the model description.

Next steps