srvar-toolkit: A Python Implementation of Shadow-Rate Vector Autoregressions with Stochastic Volatility

TL;DR

srvar-toolkit is an open-source Python package that implements Bayesian shadow-rate vector autoregressions with stochastic volatility, enabling macroeconomic forecasting when interest rates are at the effective lower bound.

Contribution

It provides a comprehensive Python toolkit implementing the methodology of Grammatikopoulos (2025) for shadow-rate VARs with stochastic volatility, including priors, data augmentation, and variable selection.

Findings

Effective macroeconomic forecasting at the lower bound.

Open-source implementation of shadow-rate VAR methodology.

Flexible Bayesian modeling with stochastic volatility.

Abstract

We introduce srvar-toolkit, an open-source Python package for Bayesian vector autoregression with shadow-rate constraints and stochastic volatility. The toolkit implements the methodology of Grammatikopoulos (2025, Journal of Forecasting) for forecasting macroeconomic variables when interest rates hit the effective lower bound. We provide conjugate Normal-Inverse-Wishart priors with Minnesota-style shrinkage, latent shadow-rate data augmentation via Gibbs sampling, diagonal stochastic volatility using the Kim-Shephard-Chib mixture approximation, and stochastic search variable selection. Core dependencies are NumPy, SciPy, and Pandas, with optional extras for plotting and a configuration-driven command-line interface. We release the software under the MIT licence at https://github.com/shawcharles/srvar-toolkit.