RMAWGEN (version 1.3.9.3)

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

Description

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

Usage

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
)

Value

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

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

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)
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