BigVAR.results {BigVAR} | R Documentation |

It inherits the class BigVAR, but contains substantially more information.

`InSampMSFE`

In-sample MSFE from optimal value of lambda

`LambdaGrid`

Grid of candidate lambda values

`index`

Rank of optimal lambda value

`OptimalLambda`

Value of lambda that minimizes MSFE

`OOSMSFE`

Average Out of sample MSFE of BigVAR model with optimal lambda

`seoosfmsfe`

Standard error of out of sample MSFE of BigVAR model with optimal lambda

`MeanMSFE`

Average out of sample MSFE of unconditional mean forecast

`MeanSD`

Standard error of out of sample MSFE of unconditional mean forecast

`MeanPreds`

predictions from conditional mean model

`RWMSFE`

Average out of sample MSFE of random walk forecast

`RWPreds`

Predictions from random walk model

`RWSD`

Standard error of out of sample MSFE of random walk forecast

`AICMSFE`

Average out of sample MSFE of AIC forecast

`AICSD`

Standard error of out of sample MSFE of AIC forecast

`AICPreds`

Predictions from AIC VAR/VARX model

`AICpvec`

Lag orders selected from AIC VAR model

`AICpvec`

Lag orders selected from AIC VARX model

`BICMSFE`

Average out of sample MSFE of BIC forecast

`BICSD`

Standard error of out of sample MSFE of BIC forecast

`BICPreds`

Predictions from BIC VAR/VARX model

`BICpvec`

Lag orders selected from BIC VAR model

`BICpvec`

Lag orders selected from BIC VARX model

`betaPred`

The final estimated

*k\times kp+ms+1*coefficient matrix, to be used for prediction`Zvals`

The final lagged values of

`Y`

, to be used for prediction`fitted`

fitted values obtained from betaPred

`resids`

residuals obtained from betaPred

`Data`

a

*T \times k*or*T\times k + m*multivariate time Series`lagmax`

Maximal lag order

`Structure`

Penalty structure

`Relaxed`

Indicator for relaxed VAR

`Granularity`

Granularity of penalty grid

`horizon`

Desired forecast horizon

`crossval`

Cross-Validation procedure

`alpha`

additional penalty parameter for Sparse Lag Group or Sparse Own/Other methods. Will contain either the heuristic choice of

*1/(k+1)*or the value selected by cross validation if the argument`dual`

is set to`TRUE`

`VARXI`

VARX Indicator

`Minnesota`

Minnesota Prior Indicator

`verbose`

verbose indicator

`dual`

indicator as to whether dual cross validation was conducted

`contemp`

indicator if contemporaneous exogenous predictors are used

`lagmatrix`

matrix of lagged values used to compute residuals (of which Zvals is the final column)

`betaArray`

array of VAR/VARX coefficients from out of sample forecasts

`sparse_count`

average fraction of active coefficients in validation period

One can also access any object of class BigVAR from BigVAR.results

Will Nicholson

[Package *BigVAR* version 1.0.6 Index]