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About

varmaker simulates observations from the vector autoregressive framework and also returns the theoretical properties of those observations.

Installation

This package has not been released to CRAN yet, but you can install the development version of varmaker with:

install.packages("devtools")
devtools::install_github("BenSmithNZL/varmaker")

Example

VAR

To simulate observations from a VAR(2) with two series, you can run:

library(varmaker)

cm_1 <- list(c(2, 0),
             matrix(c(0.5, 0.1,
                      0.4, 0.5),
                    nrow = 2,
                    ncol = 2,
                    byrow = TRUE),
             matrix(c(0, 0,
                      0.25, 0),
                    nrow = 2,
                    ncol = 2,
                    byrow = TRUE))

Sigma_a_1 <- matrix(c(0.09, 0,
                      0, 0.04),
                    nrow = 2,
                    ncol = 2,
                    byrow = TRUE)

data_1 <- create_var(cm_1, Sigma_a_1, n = 1000)

The object data_1 contains the simulated observations themselves, along with theoretical properties of the process such as the mean, autocovariance, autocorrelation, and the Granger-causalities of the series.

VMA

To simulate observations from a VMA(2) with two series, you can run:

library(varmaker)

cm_1 <- list(c(2, 0),
             matrix(c(0.5, 0.1,
                      0.4, 0.5),
                    nrow = 2,
                    ncol = 2,
                    byrow = TRUE),
             matrix(c(0, 0,
                      0.25, 0),
                    nrow = 2,
                    ncol = 2,
                    byrow = TRUE))

Sigma_a_1 <- matrix(c(0.09, 0,
                      0, 0.04),
                    nrow = 2,
                    ncol = 2,
                    byrow = TRUE)

data_2 <- create_vma(cm_1, Sigma_a_1, n = 1000)