bvar

Bayesian Macroeconometrics in R

MATLAB library for large Bayesian VARs: samplers, shrinkage priors, stochastic volatility, marginal likelihoods and forecasting, with examples, tutorials and the replication packages from joshuachan.org.

Toolkit for the estimation of hierarchical Bayesian vector autoregressions. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015). Allows for the computation of impulse responses and forecasts and provides functionality for assessing results.

Functions for Bayesian inference of vector autoregressive and vector error correction models

Bayesian Macroeconometrics C++ Library

replication code for „Forecasting with Shadow-Rate VARs“ by Carriero, Clark, Marcellino and Mertens

Standard Bayesian VAR with conjugate priors and Minnesota dummy-observation priors (unit-root and cointegration dummies) for analyzing shock transmission between U.S. 5-year and 3-year T-Bills and Colombian 5-year TES.

Python version of bvar-toolkit, the MATLAB library for large Bayesian VARs, with every function tested against its MATLAB twin

R and Python package to model Bayesian VAR and VHAR models

Bayesian Forecasting with Large Vector Autoregressions

Plugin-based macro stress testing over MCP. A person names an event; an LLM agent writes the scenario from a corpus of sources, and the person approves it. Models in levels, from the economy to industries and companies, fill in the paths and reprice positions; each model and asset type is a plugin. Output: loss quantiles, ES95, scenario price.

Bayesian hierarchical Panel VAR (PBVAR) in R (MCMC, posterior summaries, IRFs)

Macroeconomic forecasting using machine learning methods for Uzbekistan. Nine ML models (Ridge, Lasso, Elastic Net, PLS, PCR, SVR, Random Forest, XGBoost, Gradient Boosting) plus an ML ensemble againstARIMA, VAR, and BVAR benchmarks for CPI inflation and GDP growth forecasting. Python + MATLAB (IRIS Toolbox).

Use Codex to develop skills for AI agents and apply them to conduct empirical analyses using state-of-the-art methods and packages. Example: Evaluate the impact of an oil price surge on the Chinese economy.

Official vs. interpolated quarterly data in Timor-Leste: a Bayesian VAR and local-projection study of fiscal transmission and import leakage. Includes temporal disaggregation, structural breaks, IRFs/FEVDs, and a reproducible R/Shiny dashboard.

Pakistan CPI inflation forecasting: BVAR, recursive SVAR and AR — expanding-window validation, backtests, fan charts and structural IRFs (Jan 2000–Jun 2025)

Provides estimation of Bayesian vector autoregression (BVAR) models with steady-state priors via 'Stan', along with functions for unconditional and conditional forecasting, as well as impulse response analysis. For details on the steady-state BVAR model see Villani (2009) <doi:10.1002/jae.1065>.

Codes for BVHAR Research

Time series study of how EV sales affect gasoline prices in India (2015–2025), using BVAR, Granger causality, impulse responses and forecasting in R.

Final Project During Uni Era