README.md

In kavehsn/DisaggregateTS: High-Dimensional Temporal Disaggregation

DisaggregateTS

The goal of DisaggregateTS is to provide tools for temporal disaggregation, including: (1) high-dimensional and low-dimensional series generation for simulation studies; (2) a toolkit for temporal disaggregation and benchmarking using low-dimensional indicator series as proposed by Dagum and Cholette; (3) novel techniques by Mosley, Gibberd, and Eckley (2022) for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.

Installation

To install or update from CRAN

# install.packages("DisaggregateTS")
install.packages("DisaggregateTS")

You can install the development version of DisaggregateTS from GitHub with:

# install.packages("remotes")
remotes::install_github("kavehsn/DisaggregateTS")

Example

The TempDisaggDGP() generates a high-frequency response vector y, following the relationship $y = X\beta+\epsilon$, where $X$ is a matrix of indicator series and $\beta$ is a potentially sparse coefficient vector. The low-frequency vector $Y$ is generated by aggregating $y$ according to a specified aggregation method. On the other hand, the disaggregate() function contains the traditional standard-dimensional temporal disaggregation methods proposed by Denton (1971), Dagum and Cholette (2006), Chow and Lin (1971), Fernández (1981) and Litterman (1983), and the high-dimensional methods of Mosley et al. (2022).

library(DisaggregateTS)
## basic example code
data <- TempDisaggDGP(n_l=25, n=100, p=10, rho=0.5) 
X <- data$X_Gen
Y <- data$Y_Gen 
fit_chowlin <- disaggregate(Y=Y,X=X,method='Chow-Lin') 
y_hat = fit_chowlin$y_Est

Please report any bugs to Kaveh Nobari.

kavehsn/DisaggregateTS documentation built on Jan. 11, 2025, 1:25 p.m.