Package: sparsevar 1.0.0

sparsevar: Sparse VAR (Vector Autoregression) / VECM (Vector Error Correction Model) Estimation

A wrapper for sparse VAR (Vector Autoregression) and VECM (Vector Error Correction Model) time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Basu and Michailidis (2015) <doi:10.1214/15-AOS1315>.

Authors:Simone Vazzoler [aut, cre]

sparsevar_1.0.0.tar.gz
sparsevar_1.0.0.zip(r-4.7)sparsevar_1.0.0.zip(r-4.6)sparsevar_1.0.0.zip(r-4.5)
sparsevar_1.0.0.tgz(r-4.6-any)sparsevar_1.0.0.tgz(r-4.5-any)
sparsevar_1.0.0.tar.gz(r-4.7-any)sparsevar_1.0.0.tar.gz(r-4.6-any)
sparsevar_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sparsevar/json (API)

# Install 'sparsevar' in R:

install.packages('sparsevar', repos = c('https://svazzole.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/svazzole/sparsevar/issues

On CRAN:

econometrics lasso mcp scad sparse statistics time-series var vecm

6.10 score 13 stars 48 scripts 308 downloads 36 exports 38 dependencies

Last updated from:ed936d173d. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	130
source / vignettes	OK	224
linux-release-x86_64	OK	188
macos-release-arm64	OK	155
macos-oldrel-arm64	OK	179
windows-devel	OK	168
windows-release	OK	91
windows-oldrel	OK	87
wasm-release	OK	140

Exports:accuracy bootstrapped_var check_impulse_zero check_is_var companion_var compute_forecasts create_sparse_matrix decompose_pi error_bands_irf fit_var fit_varx fit_vecm frob_norm impulse_response inform_crit l_infty_norm l1norm l2norm max_norm multiplot plot_irf plot_irf_grid plot_matrix plot_var plot_vecm simulate_var simulate_varx spectral_norm spectral_radius test_granger transform_data true_negative_rate true_positive_rate var_enet var_mcp var_scad

Dependencies:backports checkmate cli codetools corpcor cpp11 doParallel farver foreach ggplot2 glmnet glue gtable isoband iterators labeling lattice lifecycle magrittr Matrix mvtnorm ncvreg plyr R6 RColorBrewer Rcpp RcppEigen reshape2 rlang S7 scales shape stringi stringr survival vctrs viridisLite withr

Using sparsevar package

Rendered fromusing.Rmdusingknitr::rmarkdown

Last update: 2026-01-27
Started: 2016-07-05

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Accuracy metric	accuracy
Bootstrap VAR	bootstrapped_var
Check Impulse Zero	check_impulse_zero
Check is var	check_is_var
Companion VAR	companion_var
Computes forecasts for VARs	compute_forecasts
Create Sparse Matrix	create_sparse_matrix
Decompose Pi VECM matrix	decompose_pi
Error bands for IRF	error_bands_irf
Multivariate VAR estimation	fit_var
Multivariate VARX estimation	fit_varx
Multivariate VECM estimation	fit_vecm
Frobenius norm of a matrix	frob_norm
Impulse Response Function	impulse_response
Computes information criteria for VARs	inform_crit
L-infinity matrix norm	l_infty_norm
L1 matrix norm	l1norm
L2 matrix norm	l2norm
Max-norm of a matrix	max_norm
Multiplots with ggplot	multiplot
IRF plot	plot_irf
IRF grid plot	plot_irf_grid
Matrix plot	plot_matrix
Plot VARs	plot_var
Plot VECMs	plot_vecm
VAR simulation	simulate_var
VARX simulation	simulate_varx
sparsevar: A package to estimate multivariate time series models (such as VAR and VECM), under the sparsity hypothesis.	sparsevar-package sparsevar
Spectral norm	spectral_norm
Spectral radius	spectral_radius
Test for Ganger Causality	test_granger
Transform data	transform_data
True Negative Rate	true_negative_rate
True Positive Rate	true_positive_rate
VAR ENET	var_enet
VAR MCP	var_mcp
VAR SCAD	var_scad