favar 0.1.1

Factor-Augmented Vector Autoregression (FAVAR) for empirical macroeconomic research

favar

favar is a Python package for estimating Factor-Augmented Vector Autoregression (FAVAR) models in empirical macroeconomic research.

It implements a two-step workflow inspired by Bernanke, Boivin, and Eliasz (2005): extract latent factors from a large information panel, estimate an augmented VAR, and compute forecasts and impulse-response functions.

Installation

pip install favar

Features

• Principal-component factor extraction from large macroeconomic panels.
• Slow-moving variable adjustment for monetary policy applications.
• Lag-order selection with AIC, BIC, FPE, and HQIC.
• FAVAR summary output with equation tables and residual correlations.
• Forecasts for observed variables with confidence intervals.
• Orthogonalized impulse-response functions.
• Panel-projected impulse responses for selected variables in the information panel.
• Residual autocorrelation diagnostics.

Quick Example

from favar import FAVAR

model = FAVAR(
    X=X,
    Y=Y,
    policy_var="policy_rate",
    k_factors=3,
    slow_columns=slow_columns,
)

results = model.fit(lags=4)

print(results.summary())

forecast = results.forecast(steps=12, confidence_level=0.95)
irf = results.impulse_response(periods=48, impulse_size=0.25)
panel_irf = results.panel_impulse_response(
    periods=48,
    columns=["industrial_production", "inflation"],
    impulse_size=0.25,
)

Documentation

The full documentation, mathematical details, synthetic walkthrough notebook, examples, and release notes are available in the GitHub repository:

https://github.com/Jonas-Santos-Siqueira/FAVAR

Citation

Bernanke, B. S., Boivin, J., & Eliasz, P. (2005). Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. Quarterly Journal of Economics.

License

MIT.