sptd: Sparse Temporal Disaggregation

In DisaggregateTS: High-Dimensional Temporal Disaggregation

Sparse Temporal Disaggregation

Description

This function performs sparse temporal disaggregation as described in \insertCite10.1111/rssa.12952;textualDisaggregateTS. It estimates the high-frequency response series using LARS (Least Angle Regression) and applies either a LASSO or adaptive LASSO penalty.

Usage

sptd(Y, X, rho, aggMat = "sum", aggRatio = 4, adaptive = FALSE)

Arguments

Y	The low-frequency response series (n_l \times 1 matrix).
X	The high-frequency indicator series (n \times p matrix).
rho	The AR(1) residual parameter (must be strictly between -1 and 1).
aggMat	Aggregation matrix method ('first', 'sum', 'average', 'last'). Default is 'sum'.
aggRatio	Aggregation ratio (e.g., 4 for annual-to-quarterly, 3 for quarterly-to-monthly). Default is 4.
adaptive	Logical. If TRUE, use adaptive LASSO penalty. If FALSE, use standard LASSO penalty. Default is FALSE.

Value

A list containing:

• y: Estimated high-frequency response series (n \times 1 matrix).

• betaHat: Estimated coefficient vector (p \times 1 matrix).

• u_l: Estimated aggregate residual series (n_l \times 1 matrix).

References

\insertAllCited

DisaggregateTS documentation built on Oct. 31, 2024, 5:09 p.m.