BigVAR.results: BigVAR.results This class contains the results from...

In BigVAR: Dimension Reduction Methods for Multivariate Time Series

BigVAR.results This class contains the results from cv.BigVAR.

Description

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

Fields

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

lambda_evolve_path

evolution of lambda over evaluation period

Note

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

Author(s)

Will Nicholson

BigVAR documentation built on Jan. 9, 2023, 5:08 p.m.