BigVAR-class {BigVAR} | R Documentation |

An object class to be used with cv.BigVAR

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

`Data`

a

*T \times k*multivariate time Series`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

`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`

)

[Package *BigVAR* version 1.0.6 Index]