bvartools: Bayesian Inference of Vector Autoregressive Models

A collection of R and C++ functions, which assist in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) models.


The package bvartools implements some common functions used for Bayesian inference for mulitvariate time series models. It should give researchers maximum freedom in setting up MCMC algorithms in R and keep calculation time limited at the same time. This is achieved by implementing posterior simulation functions in C++. Its main features are

  • The bvar and bvec functions collect the output of a Gibbs sampler in standardised objects, which can be used for further analyses.

  • Further functions such as predict, irf, fevd for forecasting, impulse response analysis and forecast error variance decomposition, respectively.

  • Computationally intensive functions - such as for posterior simulation - are written in C++ using the RcppArmadillo package of Eddelbuettel and Sanderson (2014).


Sanderson, C., & Curtin, R. (2016). Armadillo: a template-based C++ library for linear algebra. Journal of Open Source Software, 1(2), 26.

Durbin, J., & Koopman, S. J. (2002). A simple and efficient simulation smoother for state space time series analysis. Biometrika, 89(3), 603--615.

Eddelbuettel, D., & Sanderson C. (2014). RcppArmadillo: Accelerating R with high-performance C++ linear algebra. Computational Statistics and Data Analysis, 71, 1054--1063.

George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553--580.

Koop, G, & Korobilis, D. (2010), Bayesian multivariate time series Methods for empirical macroeconomics, Foundations and Trends in Econometrics, 3(4), 267--358.

Koop, G., Le<U+00F3>n-Gonz<U+00E1>lez, R., & Strachan R. W. (2010). Efficient posterior simulation for cointegrated models with priors on the cointegration space. Econometric Reviews, 29(2), 224--242.

Korobilis, D. (2013). VAR forecasting using Bayesian variable selection. Journal of Applied Econometrics, 28(2), 204--230.

L<U+00FC>tkepohl, H. (2007). New introduction to multiple time series analysis (2nd ed.). Berlin: Springer.

  • bvartools
  • bvartools-package
Documentation reproduced from package bvartools, version 0.0.1, License: GPL (>= 2)

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