Bayesian Macroeconometrics in R (BMR)

BMR (Bayesian Macroeconometrics in R) is an R interface to BM++, a templated C++ library for estimating Bayesian Vector Autoregression (BVAR) and Dynamic Stochastic General Equilibrium (DSGE) models.

Features

Several popular prior specifications:

normal-inverse-Wishart prior,
Minnesota prior, and
Mattias Villani's steady-state prior.

BMR can estimate BVARs with time-varying parameters, as well as classical (non-Bayesian) VARs.

For DSGE models, the library can
- solve models using Harald Uhlig's method of undetermined coefficients and Chris Sims' canonical decomposition;
- estimate models using MCMC with a choice of filtering methods: the Kalman filter or Chandrasekhar recursions; and
- estimate a hybrid DSGE-VAR model.

The R interface:

Provides seamless and efficient OO-based interaction using Rcpp modules.
Requires no knowledge of C++ by the user.

Author: Keith O'Hara

Download and Installation

Package requirements: R (v3.40, or above) and Rcpp, RcppArmadillo, ggplot2, and grid (this comes pre-packaged with R).

Execute the following commands, post installation of R itself, and you should be good to go:
```
install.packages("RcppArmadillo")
install.packages("ggplot2")
```

The source code is available on GitHub. You need to install BMR from source.

Perhaps the quickest way to install from source is via the 'devtools' package in R.
First, make sure you have an appropriate development environment;
- if you're using a Mac, get Xcode from the App Store and then check here;
- if you're using Windows, get Rtools.
Then simply run the following commands in R:

install.packages("devtools")
library(devtools)
install_github("kthohr/BMR")

After installing BMR, the user can check if everything is working as intended by typing:

library(BMR)
data(BMRVARData)

This should load BMR and place a dataframe called 'USMacroData' into the workspace.

Replication Files

Replication files can be found in the tests folder.