BigVAR (version 1.1.2)

VARXForecastEval: Evaluate forecasts from a VAR or VARX with lag orders selected by AIC/BIC

Description

Evaluate forecasts from a VAR or VARX with lag orders selected by AIC/BIC

Usage

VARXForecastEval(
  Y,
  X,
  p,
  s,
  T1,
  T2,
  IC,
  h,
  iterated = FALSE,
  loss = "L2",
  delta = 2.5
)

Value

Returns the one-step ahead MSFE as well as the forecasts over the evaluation period and lag order selected.

Arguments

Y

a \(T \times k\) multivariate time series

X

a \(T \times m\) multivariate time series of unmodeled exogenous variables

p

maximum lag order for endogenous series

s

maximum lag order for exogenous series

T1

start of forecast evaluation period.

T2

end of forecast evaluation period

IC

specifies whether to select lag order according to 'AIC' or 'BIC'

h

desired forecast horizon

iterated

indicator as to whether to use iterated or direct multistep forecasts (if applicable, VAR context only)

loss

loss function (default 'L2', one of 'L1','L2','Huber')

delta

delta for Huber loss function (default 2.5)

Details

This function evaluates the one-step ahead forecasts of a VAR or VARX fit by least squares over an evaluation period. At every point in time, lag orders for the endogenous and exogenous series are selected according to AIC or BIC. This function is run automatically when cv.BigVAR is called unless ic is set to FALSE in constructModel.

References

Neumaier, Arnold, and Tapio Schneider. 'Estimation of parameters and eigenmodes of multivariate autoregressive models.' ACM Transactions on Mathematical Software (TOMS) 27.1 (2001): 27-57.

See Also

VARXFit,constructModel, cv.BigVAR

Examples

data(Y)

# Evaluate the performance of a VAR with lags selected by BIC.
p <- 4
T1 <- floor(nrow(Y))/3
T2 <- floor(2*nrow(Y))/3
# Matrix of zeros for X
X <- matrix(0,nrow=nrow(Y),ncol=ncol(Y))
BICMSFE <- VARXForecastEval(Y,X,p,0,T1,T2,'BIC',1)