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
)

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

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