BigVAR-class {BigVAR}

BigVAR Object Class

Description

An object class to be used with cv.BigVAR

Details

To construct an object of class BigVAR, use the function constructModel

Slots

Data

a T \times k multivariate time series

model_data

processed time series and lag matrix

lagmax

Maximal lag order for modeled series

intercept

Indicator as to whether an intercept should be included

Structure

Penalty Structure

Relaxed

Indicator for relaxed VAR

Granularity

Granularity of penalty grid

horizon

Desired Forecast Horizon

crossval

Cross-Validation Procedure

Minnesota

Minnesota Prior Indicator

verbose

Indicator for Verbose output

dates

dates extracted from an xts object

ic

Indicator for including AIC and BIC benchmarks

VARX

VARX Model Specifications

VARXI

VARX Indicator

T1

Index of time series in which to start cross validation

T2

Index of times series in which to start forecast evaluation

ONESE

Indicator for 'One Standard Error Heuristic'

ownlambdas

Indicator for user-supplied lambdas

tf

Indicator for transfer function

alpha

Grid of candidate alpha values (applies only to Sparse VARX-L and Elastic Net models)

recursive

Indicator as to whether recursive multi-step forecasts are used (applies only to multiple horizon VAR models)

constvec

vector indicating variables to shrink toward a random walk instead of toward zero (valid only if Minnesota is TRUE)

tol

optimization tolerance

window.size

size of rolling window. If set to NULL an expanding window will be used.

separate_lambdas

indicator to use separate penalty parameter for each time series (default FALSE)

loss

Loss function to select penalty parameter (one of 'L1','L2','Huber').

delta

delta parameter for Huber loss (default 2.5)

gamma

gamma parameter for SCAD or MCP penalty (default 3)

rolling_oos

True or False: indicator to update the penalty parameter over the evaluation period (default False)

linear

indicator for linearly decrementing penalty grid (FALSE is log-linear).

refit_fraction

fraction of least squares refit to incorporate (default is 1).

See Also

constructModel