Test US monetary and fiscal shocks.

Three MCP tools

The quickest route is the hosted MCP server, with three tools: frbus_list_variables (the shockable levers, with units), frbus_shock (solve and return impulse responses), and frbus_summary (vintage and validation provenance).

Speed

A solve takes about 3 seconds cold and well under a second warm — the fastest member of the suite.

CLI mirrors

The same three tools are on the CLI, no server round-trip required.

pe-macro frbus-variables
pe-macro frbus-shock --var rffintay_aerr --shock 1.0
pe-macro frbus-summary

Units are per-lever

rffintay_aerr is in percentage points (1.0 = a 100bp tightening), but spending levers such as egfe_aerr are in log points of quarterly growth, not billions of dollars — a dollar-sized number there diverges the solver.

Each rule reads its own add-error

rffintay_aerr works only under inertial_taylor; under taylor it is rejected with a pointer to rfftay_aerr rather than silently returning all-zero responses.

Usually the point of the exercise

frbus_shock takes a policy_rule: inertial_taylor (the default, the LONGBASE rule and the one the validation numbers use), taylor, or fixed_funds_rate, which holds the funds rate on its baseline path so there is no endogenous monetary offset.

Why it matters

A four-quarter federal-purchases shock of 0.01 log points peaks higher on real GDP with the funds rate fixed — and the price-level response is larger still.

When to install

For the full 284-variable model, the Board's own demo scripts, or anything the three tools do not expose — separate from the rest of the suite, with its own Python API.

No data step

The Board's raw materials are vendored unmodified, so there is no data-download step, but you clone rather than pip-install from an index.

git clone https://github.com/PolicyEngine/us-frb-model
cd us-frb-model
uv venv && uv pip install -e ".[dev]"

The workflow

Load LONGBASE, set the Board's demo fiscal configuration, add-factor with init_trac so the baseline reproduces LONGBASE exactly, add the shock, solve.

a runnable copy of exactly this is examples/monetary_policy_shock.py. The API mirrors the essentials of pyfrbus's Frbus class — Frbus(path), .init_trac(), .solve(), .exogenize() — so the Board's demo scripts port across with minimal change.

Series you will reach for

import pandas as pd
from frbus import Frbus, load_data

data = load_data("vendor/data_only_package/LONGBASE.TXT")
model = Frbus("vendor/pyfrbus_package/models/model.xml")

start, end = pd.Period("2026Q1"), pd.Period("2030Q4")

# the fiscal-policy configuration the Board's own demos use
data.loc[start:end, "dfpdbt"] = 0
data.loc[start:end, "dfpsrp"] = 1

# add-factor the model so the baseline reproduces LONGBASE exactly
with_adds = model.init_trac(start, end, data)

# 100bp monetary policy shock in the first quarter
with_adds.loc[start, "rffintay_aerr"] += 1
sim = model.solve(start, end, with_adds)

print((sim.loc[start:end, "xgdp"] / with_adds.loc[start:end, "xgdp"] - 1) * 100)