sptd {DisaggregateTS} | R Documentation |
Sparse Temporal Disaggregation
Description
This function performs sparse temporal disaggregation as described in Mosley et al. (2022). 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 ( |
X |
The high-frequency indicator series ( |
rho |
The AR( |
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 |
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
Mosley L, Eckley IA, Gibberd A (2022). “Sparse Temporal Disaggregation.” Journal of the Royal Statistical Society Series A: Statistics in Society, 185(4), 2203-2233. ISSN 0964-1998, doi:10.1111/rssa.12952, https://academic.oup.com/jrsssa/article-pdf/185/4/2203/49420183/jrsssa_185_4_2203.pdf.