Simulates Gaussian vector autoregressive-moving-average time-series models without a burn-in period by drawing startup shocks from their model-implied conditional distribution. Also provides model test cases, autocovariances, spectral radii, and impulse responses.
varmapack simulates and analyzes Gaussian VAR, VMA, VARMA, and VARMAX
time-series models. Simulated stationary series have the correct
distribution from the first returned term, without discarding a burn-in
segment. The package also provides model testcases, theoretical and
sample autocovariances, covariance-to-correlation conversion, spectral
radii, and impulse response functions.
The Getting Started with Varmapack vignette shows model construction, simulation, testcases, and analysis. The Mathematical Description of Varmapack vignette defines the supported models and describes the simulation method.
After installing the CRAN release, open the vignettes with
vignette("getting-started", package = "varmapack") and
vignette("mathematical-description", package = "varmapack"). The
installed reference documentation is available through
help(package = "varmapack"). For information about the underlying C
library, see the C
README.
The package will be installable from CRAN when released:
install.packages("varmapack")
For the development version, install the companion randompack package
first:
remotes::install_github("jonasson2/randompack", subdir = "r-package")
remotes::install_github("jonasson2/varmapack", subdir = "r-package")
model <- varmapack_testcase("smallARMA1")
gamma <- model$acvf(maxlag = 2)
corr <- varmapack_cov2corr(gamma)
varmapack_cov2corr() converts theoretical or sample autocovariances to
correlations by dividing each entry by the product of the corresponding
lag-zero marginal standard deviations. The returned array has the same
shape as the input. Lag-zero diagonal entries are exactly one. Other
entries are not clipped to [-1,1], so correlations obtained from
lag-corrected sample autocovariances may lie outside that interval.
An additional European stock-index
example
fits a VAR(1) to the built-in EuStockMarkets log returns and generates exact
stationary simulations from the fitted model.