A wrapper for sparse VAR/VECM time series models estimation using penalties like ENET, SCAD and MCP.
Some R functions useful to estimate sparse VAR / VECM models.
To install the stable version from CRAN:
To install the developing version:
Check here to understand which are the dependencies of
devtools for your OS.
To load the
sparsevar package simply type
Using the function included in the package, we simply generate a 20x20 VAR(2) process
set.seed(1)sim <- simulateVAR(N = 20, p = 2)
This command will generate a model with two sparse matrices with 5% of non-zero entries and a Toeplitz variance-covariance matrix with rho = 0.5. We can estimate the matrices of the process using for example
fit <- fitVAR(sim$series, p = 2, threshold = TRUE)
The results can be seen by plotting the two
the first row of the plot is made by the matrices of the simulated process and the second row is formed by their estimates.
The fit contains also the estimate of the variance/covariance matrix of the residuals
which can be compared with the covariance matrix of the errors of the generating process
The functions included for model estimation are:
fitVAR: to estimate a sparse VAR multivariate time series with ENET, SCAD or MC+;
fitVECM: to estimate a sparse VECM (Vector Error Correction Model) using LS with penalty (again: ENET, SCAD or MC+);
impulseResponse: compute the impulse response function;
errorBands: estimate the error bands for the IRF (using bootstrap);
simulateVAR: to generate a sparse VAR multivariate time series;
createSparseMatrix: used to create sparse matrices with a given density;
plotMatrix: useful to plot matrices and sparse matrices;
plotVAR: plot all the matrices of the model or models in input;
plotIRF: plot IRF function;
plotGridIRF: multiple plots of IRF.
 Basu, Sumanta; Michailidis, George. Regularized estimation in sparse high-dimensional time series models. Ann. Statist. 43 (2015), no. 4, 1535--1567. doi:10.1214/15-AOS1315.