by Güney Kıymaç

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Installation

pip install SymbolicDSGE
pip install "SymbolicDSGE[fred]"  # FRED API utilities
pip install "SymbolicDSGE[sr]"    # Symbolic Regression Deps
pip install "SymbolicDSGE[ui]"    # Web based GUI

Useful Links

• Installation
• Quick Start Guide

Overview

SymbolicDSGE is a Python DSGE engine with a JIT compiled backend for linear and linearized DSGE models, supporting symbolic manipulation features for in-place model modification. It also provides measurement-equation augmentation tools, including symbolic regression for complete or restricted free-form function discovery and OLS, Ridge, Lasso, and Elastic Net for structured linear coefficient estimation. The library supports a wide set of features beyond augmentation:

• DSGE model specification, symbolic manipulation, and linearization
• Bayesian and maximum-likelihood estimation
• Simulation and impulse-response-function utilities
• Gaussian and extended Kalman filtering
• Automatic data retrieval from FRED
• Custom shock distributions via SciPy or user-defined samplers
• Monte Carlo pipelines for comparing models or model-generated samples, including:
  • Statistical tests
  • Regression setups
  • Output transformations
  • Filtering
  • Data generation

Web-UI

SymbolicDSGE ships a [ui] dependency with the goal of making DSGE basic experiments accessible to non-programmers. GUI users can:

• Create a model config
• Load and adjust existing configs
• Estimate
• Solve
• Simulate
• Create and run Monte Carlo experiments
• Apply data transformations to outputs
• Produce figures
• Access raw output data

There are two intended ways to access the GUI:

# Option 1: Serve the GUI from the command line
#           This launches an empty GUI and the configuration should be done through the Builder tab.
sdsge-ui

# Option 2: Serve the GUI with a pre-loaded SolvedModel object

# Read a config and solve the model as usual
from SymbolicDSGE import ModelParser, DSGESolver

model, kalman = ModelParser(...).get_all()
solver = DSGESolver(model, kalman)
compiled = solver.compile(...)
sol = solver.solve(compiled, ...)

# Serve the GUI with the solved model pre-loaded in the reference slot
sol.serve(open_browser=True)

Although SymbolicDSGE is still a fast-moving project; the UI effectively supports all features with the exception of integration with the [sr] extra as of v1.4.2. Symbolic Regression requires extensive configuration and templating posing unique difficulties in a no-code environment. Therefore, it's presence as a GUI component is not planned for the future.

Read the Docs

Alongside API references and implementation conventions, the documentation includes guides covering model setup, estimation, simulation, and filtering. The documentation is kept up to date and aims to clarify conventions, workflows, and implementation choices throughout SymbolicDSGE. Suggestions for improving or extending the documentation are welcome as issues.

AI Parsability

The documentation is a static Material MkDocs site, so web-enabled AI tools should be able to search, parse, and summarize it reliably.

Minimal Example

from SymbolicDSGE import ModelParser, DSGESolver

# Read the YAML config (Equations, Measurements, Parameters, Optional Filter Spec)
parsed = ModelParser("<path-to-config>.yaml").get_all()
model, kalman = parsed

# Compile the model
solver = DSGESolver(model, kalman)
compiled = solver.compile()

print("Equations with symbols removed: \n", "\n".join(map(str, compiled.objective_eqs)), "\n")

>>> Equations with symbols removed:
 -beta*fwd_Pi + cur_Pi - cur_x*kappa - cur_z
-cur_g + cur_x - fwd_x + tau_inv*(cur_r - fwd_Pi)
-cur_r*rho_r - e_R + fwd_r + (rho_r - 1)*(fwd_Pi*psi_pi + fwd_x*psi_x)
-cur_g*rho_g - e_g + fwd_g
-cur_z*rho_z - e_z + fwd_z

# Solve the compiled model
sol = solver.solve(
    compiled,
    parameters=None,
    ss_seed=[0.0, 0.0, 0.0, 0.0, 0.0],
)
print("Is stable: ", sol.policy.stab == 0)
print("Eigenvalues: ", sol.policy.eig)

>>> Is stable:  True
Eigenvalues:  [0.28 +0.j 0.83 +0.j 0.85 +0.j 2.605+0.j 1.185+0.j]

# Plot IRFs (single or multi shock)
sol.transition_plot(
    T=25,
    shocks=["g", "z"],
    scale=1.0,
    observables=True,
)