# bvartools

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##### 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.

##### Details

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).

##### References

Sanderson, C., & Curtin, R. (2016). Armadillo: a template-based C++ library for linear algebra. Journal of Open Source Software, 1(2), 26. http://dx.doi.org/10.21105/joss.00026

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. http://dx.doi.org/10.1016/j.csda.2013.02.005

George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553--580. https://doi.org/10.1016/j.jeconom.2007.08.017

Koop, G, & Korobilis, D. (2010), Bayesian multivariate time series Methods for empirical macroeconomics, Foundations and Trends in Econometrics, 3(4), 267--358. http://dx.doi.org/10.1561/0800000013

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. https://doi.org/10.1080/07474930903382208

Korobilis, D. (2013). VAR forecasting using Bayesian variable selection. Journal of Applied Econometrics, 28(2), 204--230. https://doi.org/10.1002/jae.1271

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

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

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