An R package for Bayesian Estimation of Structural Vector Autoregressive Models
Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity and hypotheses on autoregressive parameters, and analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024), Lütkepohl & Woźniak (2020), Song & Woźniak (2021), and Woźniak & Droumaguet (2015).
- All the models in the bsvars package consist of the Vector
Autoregressive equation, with autoregressive parameters
Aand error termsE, and the structural equation with structural shocksU
Y = AX + E (VAR equation)
BE = U (structural equation)
- The models are identified via exclusion restrictions, heteroskedasticity, or non-normality
- Autoregressive parameters
Aand the structural matrixBare feature a three-level local-global hierarchical prior that estimates the equation-specific level of shrinkage - In five models the structural shocks are conditionally normal with
zero mean and diagonal covariance matrix with variances that are:
- equal to one
- time-varying following non-centred Stochastic Volatility
- time-varying following centred Stochastic Volatility
- time-varying with stationary Markov Switching
- time-varying with sparse Markov Switching where the number of volatility regimes is estimated
- In two more models non-normal structural shocks following
- a finite mixture of normal components and component-specific variances
- a sparse mixture of normal components and component-specific variances where the number of states is estimated
- Specify the models using
specify_bsvar_*()functions, for instance,specify_bsvar() - Estimate the models using the
estimate()method - Predict the future using the
forecast()method - Provide structural analyses using impulse responses, forecast
error variance decompositions, historical decompositions, and
structural shocks using functions
compute_impulse_responses(),compute_variance_decompositions(),compute_historical_decompositions(), andcompute_structural_shocks()respectively - Analyse the fitted values, time-varying volatility, and volatility
regimes using functions
compute_fitted_values(),compute_conditional_sd(), andcompute_regime_probabilities()respectively - Use
plot()andsummary()methods to gain the insights into the core of the empirical problem.
- Extraordinary computational speed is obtained by combining
- the implementation of frontier econometric techniques, and
- compiled code written in cpp
- It combines the best of two worlds: the ease of data analysis with R and fast cpp algorithms
- The algorithms used here are very fast. But still, Bayesian estimation might take a little time. Look at our beautiful progress bar in the meantime:
**************************************************|
bsvars: Bayesian Structural Vector Autoregressions|
**************************************************|
Gibbs sampler for the SVAR-SV model |
Non-centred SV model is estimated |
**************************************************|
Progress of the MCMC simulation for 1000 draws
Every 10th draw is saved via MCMC thinning
Press Esc to interrupt the computations
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
*************************************
This beautiful logo can be reproduced in R using this file.
The beginnings are as easy as ABC:
library(bsvars) # upload the package
data(us_fiscal_lsuw) # upload data
spec = specify_bsvar_sv$new(us_fiscal_lsuw, p = 4) # specify the model
burn_in = estimate(spec, 1000) # run the burn-in
out = estimate(burn_in, 50000) # estimate the modelStarting from bsvars version 2.0.0 a simplified workflow using the
|> pipe is possible:
library(bsvars) # upload the package
data(us_fiscal_lsuw) # upload data
us_fiscal_lsuw |>
specify_bsvar_sv$new(p = 4) |> # specify the model
estimate(S = 1000) |> # run the burn-in
estimate(S = 50000) -> out # estimate the modelNow, you’re ready to analyse your model!
You must have a cpp compiler. Follow the instructions from Section 1.3. by Eddelbuettel & François (2023). In short:
Windows: install RTools.
macOS: install Xcode Command Line Tools.
Linux: install the standard developement packages.
Just open your R and type:
install.packages("bsvars")
The developer’s version of the package with the newest features can be installed by typing:
devtools::install_git("https://github.com/bsvars/bsvars.git")
The package is under intensive development. Your help is welcome! Please, have a look at the roadmap, discuss package features and applications, or report a bug. Thank you!
Tomasz is a Bayesian econometrician and a Senior Lecturer at the University of Melbourne. He develops methodology for empirical macroeconomic analyses and programs in R and cpp using Rcpp.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent