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Impulso

Bayesian Vector Autoregression in Python.

import pandas as pd
from impulso import VAR, VARData
from impulso.identification import Cholesky

# Load data
df = pd.read_csv("macro_data.csv", index_col="date", parse_dates=True)
data = VARData.from_df(df, endog=["gdp", "inflation", "rate"])

# Estimate
fitted = VAR(lags="bic", prior="minnesota").fit(data)

# Forecast
forecast = fitted.forecast(steps=8)
forecast.median()  # point forecasts
forecast.hdi()     # credible intervals

# Structural analysis
identified = fitted.set_identification_strategy(
    Cholesky(ordering=["gdp", "inflation", "rate"])
)
irf = identified.impulse_response(horizon=20)
irf.plot()

Features

  • Validated data containers — VARData catches shape mismatches, missing values, and type errors at construction time
  • Immutable pipeline — VARData -> VAR -> FittedVAR -> IdentifiedVAR, each stage frozen after creation
  • Economist-friendly API — think in variables and lags, not tensors and MCMC chains
  • Minnesota prior — smart defaults with tunable hyperparameters for shrinkage
  • Automatic lag selection — AIC, BIC, and Hannan-Quinn criteria
  • PyMC backend — full Bayesian estimation with NUTS sampling
  • Probabilistic forecasts — posterior median, HDI credible intervals, tidy DataFrames
  • Structural identification — Cholesky and sign restriction schemes
  • Built-in plotting — IRF, FEVD, forecast, and historical decomposition plots

Installation

pip install impulso

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