bvar

Bayesian Macroeconometrics in R

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

R and Python package to model Bayesian VAR and VHAR models

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.

Bayesian Forecasting with Large Vector Autoregressions

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.

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

Final Project During Uni Era