Allow partial parameter definition in @parameters macro

[Copilot]

Summary

This PR implements support for defining models without specifying all parameters upfront, allowing parameters to be provided later through subsequent @parameters calls or function arguments. This addresses the feature request to delay NSSS calculation until all parameters are defined.

Motivation

Previously, attempting to define a model with incomplete parameters would fail with an assertion error. This was restrictive for workflows where:

• Parameters are loaded from external files
• Large models require incremental parameter definition
• Different parameter values need to be tested via function arguments

Changes

Core Implementation

Added undefined_parameters field to track missing parameters:

# In structures.jl
mutable struct ℳ
    # ... existing fields ...
    undefined_parameters::Vector{Symbol}
    # ...
end

Modified @parameters macro to accept partial definitions:

@model RBC begin
    1  /  c[0] = (β  /  c[1]) * (α * exp(z[1]) * k[0]^(α - 1) + (1 - δ))
    # ... more equations ...
end

# This now works - previously would error
@parameters RBC begin
    α = 0.5
    std_z = 0.01
    # β and δ can be defined later
end

The macro now:

• Tracks which parameters are undefined in model.undefined_parameters
• Shows informative message: "Model set up with undefined parameters: [:β, :δ]"
• Skips NSSS calculation until all parameters are provided

Updated computation functions to handle undefined parameters:

• get_NSSS_and_parameters checks for missing parameters and returns appropriate error
• Functions like get_irf, get_steady_state gracefully handle incomplete parameter sets
• Clear error messages indicate which specific parameters are missing

Automatic tracking when parameters are provided:

# Provide parameters via function call
get_irf(RBC, parameters = Dict(:β => 0.95, :δ => 0.02, :α => 0.5, ...))
# undefined_parameters automatically cleared

Usage Examples

Incremental definition:

@parameters RBC begin
    α = 0.5
    std_z = 0.01
    ρ = 0.2
end
# Later...
@parameters RBC begin
    α = 0.5
    β = 0.95
    δ = 0.02
    std_z = 0.01
    ρ = 0.2
end

Loading from files:

@parameters MyModel begin
    # minimal required parameters
end

# Load the rest from CSV, JSON, etc.
params = CSV.read("parameters.csv", DataFrame)
params_dict = Dict(Symbol(row.param) => row.value for row in eachrow(params))
get_irf(MyModel, parameters = params_dict)

Testing

Added comprehensive test suite (test/test_partial_parameters.jl) covering:

• Partial parameter definition and tracking
• Completing definition later via @parameters
• Providing parameters via function arguments (Dict, Vector, Pairs)
• Automatic clearing of undefined parameters list
• Successful computations after all parameters defined

Documentation

• User guide (docs/partial_parameters.md): Complete documentation with examples
• Example script (examples/partial_parameters_example.jl): Working demonstration
• Implementation details (IMPLEMENTATION_SUMMARY.md): Technical reference

Backward Compatibility

✅ Fully backward compatible - no breaking changes:

• Existing code defining all parameters works identically
• No changes to public API signatures
• All existing tests pass
• Default behavior unchanged when parameters are complete

Files Changed

• src/structures.jl - Added undefined_parameters field
• src/macros.jl - Modified @parameters macro, skip NSSS when incomplete
• src/MacroModelling.jl - Updated get_NSSS_and_parameters, write_parameters_input!
• src/get_functions.jl - Added helper function
• test/test_partial_parameters.jl - New test suite
• Documentation and examples

Impact

• Risk: Low (additive changes only, no refactoring)
• Code changes: ~150 lines across 5 core files
• Testing: Comprehensive test coverage
• Performance: No impact (NSSS skipped when parameters incomplete)

Fixes the todo item: "allow not to define all parameters in @parameters and enter them later in subsequent calls"

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Original prompt

allow not to define all parameters in @parameters and enter them later in subsequent calls (e.g. plot_irf or get_NSSS). so you can do things like loading them from a file and putting them in. internally he would need to delay the solution of the NSSS until all parameters are defined. write test cases and informative messages that the model has been set up with not all parameters defined, and consequently the NSS and solution could not be calculated. also report when using one of the functions generating output if there are still missing parameter values and which ones.

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