multivar-class {multivar}

multivar object class

Description

An object class to be used with cv.multivar

Details

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

Slots

k

Numeric. The number of subjects (or groupings) in the dataset.

n

Numeric Vector. Vector containing the number of timepoints for each dataset.

d

Numeric Vector. Vector containing the number of variables for each dataset.

Ak

List. A list (length = k) of lagged (T-lag-horizon) by d multivariate time series.

bk

List. A list (length = k) of (T-lag-horizon) by d multivariate time series.

Hk

List. A list (length = k) of (horizon) by d multivariate time series.

A

Matrix. A matrix containing the lagged ((T-lag-horizon)k) by (d+dk) multivariate time series.

b

Matrix. A matrix containing the non-lagged ((T-lag-horizon)k) by (d) multivariate time series.

H

Matrix. A matrix containing the non-lagged (horizon k) by d multivariate time series.

lag

Numeric. The VAR order. Currently only lag 1 is supported.

horizon

Numeric. Forecast horizon.

t1

Numeric vector. Index of time series in which to start cross validation for individual k.

t2

Numeric vector. Index of time series in which to end cross validation for individual k.

lambda1

Numeric vector. Regularization parameter 1.

lambda2

Numeric vector. Regularization parameter 2.

nlambda1

Numeric. Number of lambda1 values to search over. Default is 30.

nlambda2

Numeric. Number of lambda2 values to search over. Default is 30.

tol

Numeric. Convergence tolerance.

depth

Numeric. Depth of grid construction. Default is 1000.

window

Numeric. Size of rolling window.

standardize

Logical. Default is true. Whether to standardize the individual data.

weightest

Character. How to estimate the first-stage weights. Default is "lasso". Other options include "ridge", "ols" and "var".

canonical

Logical. Default is false. If true, individual datasets are fit to a VAR(1) model.

threshold

Logical. Default is false. If true, and canonical is true, individual transition matrices are thresholded based on significance.

lassotype

Character. Default is "adaptive". Choices are "standard" or "adaptive" lasso.

intercept

Logical. Default is FALSE.

W

Matrix. Default is NULL.

ratios

Numeric vector. Default is NULL.

cv

Character. Default is "blocked" for k-folds blocked cross-validation. rolling window cross-validation also available using "rolling". If "blocked" is selected the nfolds argument should be specified.

nfolds

Numeric. The number of folds for use with "blocked" cross-validation.

thresh

Numeric. Post-estimation threshold for setting the individual-level coefficients to zero if their absolute value is smaller than the value provided. Default is zero.

lamadapt

Logical. Should the lambdas be calculated adaptively. Default is FALSE.

See Also

constructModel