Either creates a VAR model or chooses a VAR model by using VAR or VARselect commands of vars package

Either creates a VAR model or chooses a VAR model by using VAR or VARselect commands of vars package

getVARmodel(
  data,
  suffix = c("_Tx", "_Tn"),
  sep = "",
  p = 1,
  type = "none",
  season = NULL,
  exogen = NULL,
  lag.max = NULL,
  ic = "AIC",
  activateVARselect = FALSE,
  na.rm = TRUE,
  n_GPCA_iteration = 0,
  n_GPCA_iteration_residuals = n_GPCA_iteration,
  extremes = TRUE,
  nearPD = FALSE
)

Arguments

data: see VAR and addsuffixes
suffix: see addsuffixes
sep: separator element. See addsuffixes).
p: lag considered for the auto-regression see VAR
type: see VAR
season: see VAR
exogen: see VAR
lag.max: see VARselect
ic: see VAR
activateVARselect: logical variables. If TRUE, the function VARselect is run. Default and recommended use is FALSE.
na.rm: logical variables. If TRUE (default), it takes into account NA values
n_GPCA_iteration: number of iterations of Gaussianization process for data. Default is 0 (no Gaussianization)
n_GPCA_iteration_residuals: number of iterations of Gaussianization process for data. Default is 0 (no Gaussianization)
extremes: see normalizeGaussian_severalstations and GPCA
nearPD: logical (experimental) and passed to GPCA. Default is FALSE. If TRUE covariance matrix is corrected through Nearest Positive Definite Matrix procedure, i.e. nearPD

Value

a varest2 or GPCAvarest2 object representing a VAR model or a GPCA-varest object which also contains the GPCA transformation parameters

Note

It inherits input parameters of VAR, VARselect and addsuffixes. The variable data contains the measured data on which the vector auto-regressive models is estimated. It is a matrix where each row is a realization of the vector random variable. In some application of this package, the random variables may be the daily maximum and minimum temperature anomalies for different stations. Often the the columns of data are called with the IDs of the stations whithout specifying the type of variable (e.g. minimun or maximum temperature anomalies). This means that two or more columns may have the same name. Therefore the function addsuffixes, which is called from this function, adds suitable suffixes to the column names.

Author

Emanuele Cordano, Emanuele Eccel

Examples


set.seed(122)
NSTEP <- 1000
x <- rnorm(NSTEP)
y <- x+rnorm(NSTEP)
z <- c(rnorm(1),y[-1]+rnorm(NSTEP-1))
df <- data.frame(x=x,y=y,z=z)

exogen <- as.data.frame(x+5)
only_var <- VAR(df,type="none")

gpcavar <- getVARmodel(data=df,suffix=NULL,p=3,n_GPCA_iteration=5,
                       n_GPCA_iteration_residuals=5,exogen=exogen)
#> Warning: No column names supplied in exogen, using: exo1 , instead.
exogen <- as.data.frame(x+5)

data_for_var <- gpcavar@GPCA_data$final_results

data(collinear_dataset)
gpcavar_coll <- getVARmodel(data=collinear_dataset,suffix=NULL,p=3,n_GPCA_iteration=5,
                       n_GPCA_iteration_residuals=0,exogen=NULL,nearPD=TRUE) ## use nearPD==TRUE