Introduction to bvartools

Introduction

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 an MCMC algorithm 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 function collects 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).¹

This vignette provides the code to set up and estimate a basic Bayesian VAR (BVAR) model with the bvartools package.² For this illustration the dataset E1 from Lütkepohl (2007) is used. It contains data on West German fixed investment, disposable income and consumption expenditures in billions of DM from 1960Q1 to 1982Q4.

library(bvartools)

data("e1")
e1 <- diff(log(e1))

plot(e1) # Plot the series

The gen_var function produces the inputs y and x for the BVAR estimator, where y is a matrix of dependent variables and x is the matrix of regressors for the model

\[y_t = A x_t + u_t,\] with \(u_t \sim N(0, \Sigma)\).

data <- gen_var(e1, p = 2, deterministic = "const")

y <- data$Y[, 1:73]
x <- data$Z[, 1:73]

As in Lütkepohl (2007) only the first 73 observations are used.

Estimation

A_freq <- tcrossprod(y, x) %*% solve(tcrossprod(x)) # Calculate estimates
round(A_freq, 3) # Round estimates and print
#>        invest.1 income.1 cons.1 invest.2 income.2 cons.2  const
#> invest   -0.320    0.146  0.961   -0.161    0.115  0.934 -0.017
#> income    0.044   -0.153  0.289    0.050    0.019 -0.010  0.016
#> cons     -0.002    0.225 -0.264    0.034    0.355 -0.022  0.013

u_freq <- y - A_freq %*% x
u_sigma_freq <- tcrossprod(u_freq) / (ncol(y) - nrow(x))
round(u_sigma_freq * 10^4, 2)
#>        invest income cons
#> invest  21.30   0.72 1.23
#> income   0.72   1.37 0.61
#> cons     1.23   0.61 0.89

# Reset random number generator for reproducibility
set.seed(1234567)

iter <- 10000 # Number of iterations of the Gibbs sampler
burnin <- 5000 # Number of burn-in draws
store <- iter - burnin

t <- ncol(y) # Number of observations
k <- nrow(y) # Number of endogenous variables
m <- k * nrow(x) # Number of estimated coefficients

# Set (uninformative) priors
a_mu_prior <- matrix(0, m) # Vector of prior parameter means
a_v_i_prior <- diag(0, m) # Inverse of the prior covariance matrix

u_sigma_df_prior <- 0 # Prior degrees of freedom
u_sigma_scale_prior <- diag(0, k) # Prior covariance matrix
u_sigma_df_post <- t + u_sigma_df_prior # Posterior degrees of freedom

# Initial values
u_sigma <- diag(.00001, k)
u_sigma_i <- solve(u_sigma)

# Data containers for posterior draws
draws_a <- matrix(NA, m, store)
draws_sigma <- matrix(NA, k^2, store)

# Start Gibbs sampler
for (draw in 1:iter) {
  # Draw conditional mean parameters
  a <- post_normal(y, x, u_sigma_i, a_mu_prior, a_v_i_prior)
  
  # Draw variance-covariance matrix
  u <- y - matrix(a, k) %*% x # Obtain residuals
  u_sigma_scale_post <- solve(u_sigma_scale_prior + tcrossprod(u))
  u_sigma_i <- matrix(rWishart(1, u_sigma_df_post, u_sigma_scale_post)[,, 1], k)
  u_sigma <- solve(u_sigma_i) # Invert Sigma_i to obtain Sigma
  
  # Store draws
  if (draw > burnin) {
    draws_a[, draw - burnin] <- a
    draws_sigma[, draw - burnin] <- u_sigma
  }
}

A <- rowMeans(draws_a) # Obtain means for every row
A <- matrix(A, k) # Transform mean vector into a matrix
A <- round(A, 3) # Round values
dimnames(A) <- list(dimnames(y)[[1]], dimnames(x)[[1]]) # Rename matrix dimensions

A # Print
#>        invest.1 income.1 cons.1 invest.2 income.2 cons.2  const
#> invest   -0.321    0.143  0.971   -0.161    0.099  0.938 -0.016
#> income    0.044   -0.152  0.287    0.050    0.019 -0.008  0.016
#> cons     -0.003    0.224 -0.262    0.033    0.357 -0.022  0.013

Sigma <- rowMeans(draws_sigma) # Obtain means for every row
Sigma <- matrix(Sigma, k) # Transform mean vector into a matrix
Sigma <- round(Sigma * 10^4, 2) # Round values
dimnames(Sigma) <- list(dimnames(y)[[1]], dimnames(y)[[1]]) # Rename matrix dimensions

Sigma # Print
#>        invest income cons
#> invest  22.68   0.78 1.32
#> income   0.78   1.46 0.66
#> cons     1.32   0.66 0.95

bvar objects

The bvar function can be used to collect relevant output of the Gibbs sampler into a standardised object, which can be used by further functions such as predict to obtain forecasts or irf for impulse respons analysis.

bvar_est <- bvar(y = y, x = x, A = draws_a[1:18,],
                 C = draws_a[19:21, ], Sigma = draws_sigma)

Posterior draws can be thinned with function thin:

bvar_est <- thin(bvar_est, thin = 5)

Forecasts

Forecasts with credible bands can be obtained with the function predict. If the model contains deterministic terms, new values can be provided in the argument new_D. If no values are provided, the function sets them to zero. The number of rows of new_D must be the same as the argument n.ahead.

bvar_pred <- predict(bvar_est, n.ahead = 10, new_D = rep(1, 10))

plot(bvar_pred)

Impulse response analysis

Currently, bvartools supports forecast error, orthogonalised, and generalised impulse response functions.

FEIR <- irf(bvar_est, impulse = "income", response = "cons", n.ahead = 8)

plot(FEIR, main = "Forecast Error Impulse Response", xlab = "Period", ylab = "Response")

OIR <- irf(bvar_est, impulse = "income", response = "cons", n.ahead = 8, type = "oir")

plot(OIR, main = "Orthogonalised Impulse Response", xlab = "Period", ylab = "Response")

GIR <- irf(bvar_est, impulse = "income", response = "cons", n.ahead = 8, type = "gir")

plot(GIR, main = "Generalised Impulse Response", xlab = "Period", ylab = "Response")

Forecast error variance decomposition

Default forecast error variance decomposition (FEVD) is based on orthogonalised impulse responses (OIR).

bvar_fevd_oir <- fevd(bvar_est, response = "cons")

plot(bvar_fevd_oir, main = "OIR-based FEVD of consumption")

It is also possible to calculate FEVDs, which are based on generalised impulse responses (GIR). Note that these do not automatically add up to unity.

bvar_fevd_gir <- fevd(bvar_est, response = "cons", type = "gir")

plot(bvar_fevd_gir, main = "GIR-based FEVD of consumption")

References

Eddelbuettel, D., & Sanderson C. (2014). RcppArmadillo: Accelerating R with high-performance C++ linear algebra. Computational Statistics and Data Analysis, 71, 1054-1063. https://doi.org/10.1016/j.csda.2013.02.005

Koop, G., Pesaran, M. H., & Potter, S.M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics 74(1), 119-147. https://doi.org/10.1016/0304-4076(95)01753-4

Lütkepohl, H. (2007). New introduction to multiple time series analysis (2nd ed.). Berlin: Springer.

Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29. https://doi.org/10.1016/S0165-1765(97)00214-0

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