BigVAR (version 1.0)

constructModel: Construct an object of class BigVAR

Description

Construct an object of class BigVAR

Usage

constructModel(Y, p, struct, gran, RVAR = FALSE, h = 1, cv = "Rolling",
  MN = FALSE, verbose = TRUE, IC = TRUE, VARX = list(),
  T1 = floor(nrow(Y)/3), T2 = floor(2 * nrow(Y)/3), ONESE = FALSE,
  ownlambdas = FALSE, alpha = as.double(NULL), recursive = FALSE)

Arguments

Y

$T \times k$ multivariate time series or Y $T \times (k+m)$ endogenous and exogenous series, respectively

p

Predetermined maximal lag order (for modeled series)

struct

The choice of penalty structure (see details).

gran

vector of penalty parameter specifications.

RVAR

True or False: whether to refit based upon the support selected using the Relaxed-VAR procedure

h

Desired forecast horizon

cv

Cross-validation approach, either "Rolling" for rolling cross-validation or "LOO" for leave-one-out cross-validation.

MN

Minnesota Prior Indicator

verbose

Verbose output while estimating

IC

True or False: whether to include AIC and BIC benchmarks

VARX

List containing VARX model specifications.

T1

Index of time series in which to start cross validation

T2

Index of times series in which to start forecast evaluation

ONESE

True or False: whether to use the "One Standard Error Heuristic"

ownlambdas

True or False: Indicator for user-supplied penalty parameters

alpha

grid of candidate parameters for the alpha in the Sparse Lag and Sparse Own/Other VARX-L

recursive

True or False: Indicator as to whether iterative multi-step predictions are desired in the VAR context if the forecast horizon is greater than 1

code

BigVAR.est

Details

The choices for "struct" are as follows

• "Basic" (Basic VARX-L)

"Lag" (Lag Group VARX-L) "SparseLag" (Lag Sparse Group VARX-L) "OwnOther" (Own/Other Group VARX-L) "SparseOO" (Own/Other Sparse Group VARX-L) "EFX" (Endogenous First VARX-L) "HVARC" (Componentwise HVAR) "HVAROO" (Own/Other HVAR) "HVARELEM" (Elementwise HVAR) "Tapered" (Lag weighted Lasso VAR)

References

William B Nicholson, Jacob Bien, and David S Matteson. "High Dimensional Forecasting via Interpretable Vector Autoregression." arXiv preprint arXiv:1412.5250, 2016. William B Nicholson, David S. Matteson, and Jacob Bien (2015), "VARX-L Structured regularization for large vector autoregressions with exogenous variables," arXiv preprint arXiv:1508.07497, 2016. William B Nicholson, David S. Matteson, and Jacob Bien (2016), "BigVAR: Dimension Reduction Reduction Methods for Multivariate Time Series," http://www.wbnicholson.com/BigVAR.pdf.

See Also

cv.BigVAR,BigVAR.est

Examples

# VARX Example
# Create a Basic VARX-L with k=2, m=1, s=2, p=4
VARX=list()
VARX$k=2 # indicates that the first two series are modeled
VARX$s=2 # sets 2 as the maximal lag order for exogenous series
data(Y)
T1=floor(nrow(Y)/3)
T2=floor(2*nrow(Y)/3)
Model1=constructModel(Y,p=4,struct="Basic",gran=c(50,10),verbose=FALSE,VARX=VARX,T1=T1,T2=T2)