Multivariate VAR estimation
A function to estimate a (possibly high-dimensional) multivariate VAR time series using penalized least squares methods, such as ENET, SCAD or MC+.
fitVAR(data, p = 1, penalty = "ENET", method = "cv", ...)
data: the data from the time series: variables in columns and observations in rowsp: order of the VAR modelpenalty: the penalty function to use. Possible values are "ENET", "SCAD" or "MCP"method: possible values are "cv" or "timeSlice"...: the options for the estimation. Global options are: threshold: if TRUE all the entries smaller than the oracle threshold are set to zero; scale: scale the data (default = FALSE)? nfolds: the number of folds used for cross validation (default = 10); parallel: if TRUE use multicore backend (default = FALSE); ncores: if parallel is TRUE, specify the number of cores to use for parallel evaluation. Options for ENET estimation: alpha: the value of alpha to use in elastic net (0 is Ridge regression, 1 is LASSO (default)); type.measure: the measure to use for error evaluation ("mse" or "mae"); nlambda: the number of lambdas to use in the cross validation (default = 100); leaveOut: in the time slice validation leave out the last leaveOutLast observations (default = 15); horizon: the horizon to use for estimating mse/mae (default = 1); picasso: use picasso package for estimation (only available for penalty = "SCAD" and method = "timeSlice").A the list (of length p) of the estimated matrices of the process
fit the results of the penalized LS estimation
mse the mean square error of the cross validation
time elapsed time for the estimation
residuals the time series of the residuals