hdBIC {DisaggregateTS}R Documentation

High-dimensional BIC score

Description

This function calculates a BIC score that performs better than the ordinary BIC in high-dimensional scenarios. It uses the variance estimator given in Yu and Bien (2019).

Usage

hdBIC(X, Y, covariance, beta)

Arguments

X

Aggregated indicator series matrix that has been GLS rotated (an n_l \times p matrix).

Y

Low-frequency response vector that has been GLS rotated (an n_l \times 1 vector).

covariance

Aggregated AR covariance matrix (an n_l \times n_l matrix).

beta

Estimate of the regression coefficients (a p \times 1 vector).

Value

      The BIC score for model comparison.

References

Yu G, Bien J (2019). “Estimating the error variance in a high-dimensional linear model.” Biometrika, 106(3), 533–546.


[Package DisaggregateTS version 3.0.1 Index]