srvar-toolkit¶
Shadow-rate VAR toolkit for Bayesian macroeconomic forecasting in pure Python.
A lightweight, tested implementation of Shadow-Rate Vector Autoregression models with ELB data augmentation, multiple Minnesota-style shrinkage paths, stochastic volatility, structural analysis, and a config-driven CLI workflow with backtesting. The library also includes FRED data fetching for building reproducible macro datasets.
pip install git+https://github.com/shawcharles/srvar-toolkit.git
Note
Version 0.3.0 adds explicit legacy/canonical/tempered Minnesota prior modes, missing-data-safe
evaluation/plotting, and a larger benchmark/diagnostic toolkit for comparing Minnesota variants.
Installation instructions and a quickstart tutorial to fit your first Bayesian VAR.
Core concepts, configuration options, glossary of terms, and known limitations.
Statistical methodology: shadow-rate VARs, ELB constraints, stochastic volatility, and MCMC sampling.
Complete function and class documentation with type signatures and examples.
Features¶
Component |
Description |
Status |
|---|---|---|
Conjugate BVAR (NIW) |
Closed-form posterior updates |
✅ Supported |
Legacy Minnesota-style NIW shrinkage |
Historical prior construction with lag decay (non-canonical) |
✅ Supported |
Canonical Minnesota shrinkage |
Equation-wise own-vs-cross shrinkage for homoskedastic and diagonal SV models |
✅ Supported |
Tempered Minnesota bridge |
Experimental legacy-to-canonical bridge for diagonal SV |
⚠️ Experimental |
Shadow-Rate / ELB |
Latent shadow-rate sampling |
✅ Supported |
Stochastic Volatility |
Diagonal SV (RW/AR1), triangular covariance SV, and factor SV |
✅ Supported |
Variable Selection (SSVS) |
Spike-and-slab priors |
✅ Supported |
Bayesian LASSO (BLASSO) |
Shrinkage prior for VAR coefficients |
✅ Supported |
Steady-State VAR (SSP) |
Parameterize intercept via steady-state mean |
✅ Supported |
Forecasting |
Posterior predictive simulation |
✅ Supported |
Backtesting + Evaluation |
Rolling/expanding backtests with metrics, plots, and streaming evaluation |
✅ Supported |
Conditional forecasts |
Scenario forecasts with hard constraints |
✅ Supported |
Structural IRFs / FEVD / HD |
Cholesky and sign-restricted structural analysis from posterior draws |
✅ Supported |