shrink_oasd

Shrinkage of the covariance matrix according to the Oracle Approximating Shrinkage (OAS)
of Chen et al. (2009) and Ando and Xiao (2023).

Classical (bottom-up and top-down), optimal combination and heuristic
point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and
probabilistic (Girolimetto et al. 2024 <doi:10.1016/j.ijforecast.2023.10.003>)
forecast reconciliation procedures for linearly constrained time series
(e.g., hierarchical or grouped time series) in cross-sectional, temporal,
or cross-temporal frameworks.

Daniele Girolimetto

FoReco

Forecast Reconciliation

Tommaso Di Fonzo

shrink_oasd function

<dl><dt>x</dt>
<dd>A numeric matrix containing the in-sample residuals.</dd>
<dt>mse</dt>
<dd>If <code>TRUE</code> (default), the residuals used to compute the covariance
matrix are not mean-corrected.</dd></dl>

Arguments

Shrinkage of the covariance matrix using the Oracle approximation — shrink_oasd

<dl>

<dt>x</dt>
<dd>A numeric matrix containing the in-sample residuals.</dd>


<dt>mse</dt>
<dd>If <code>TRUE</code> (default), the residuals used to compute the covariance
matrix are not mean-corrected.</dd>

</dl>

shrink_oasd: Shrinkage of the covariance matrix using the Oracle approximation

Description

Usage

Value

Arguments

References

See Also