disaggregate: Temporal Disaggregation Methods

In DisaggregateTS: High-Dimensional Temporal Disaggregation

Temporal Disaggregation Methods

Description

This function contains the traditional standard-dimensional temporal disaggregation methods proposed by \insertCitedenton1971adjustment;textualDisaggregateTS, \insertCitedagum2006benchmarking;textualDisaggregateTS, \insertCitechow1971best;textualDisaggregateTS, \insertCitefernandez1981methodological;textualDisaggregateTS and \insertCitelitterman1983random;textualDisaggregateTS, and the high-dimensional methods of \insertCite10.1111/rssa.12952;textualDisaggregateTS.

Usage

disaggregate(
  Y,
  X = matrix(data = rep(1, times = (nrow(Y) * aggRatio)), nrow = (nrow(Y) * aggRatio)),
  aggMat = "sum",
  aggRatio = 4,
  method = "Chow-Lin",
  Denton = "additive-first-diff"
)

Arguments

Y	The low-frequency response series (n_l \times 1 matrix).
X	The high-frequency indicator series (n \times p matrix).
aggMat	Aggregation matrix according to '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).
method	Disaggregation method using 'Denton', 'Denton-Cholette', 'Chow-Lin', 'Fernandez', 'Litterman', 'spTD' or 'adaptive-spTD' (default is 'Chow-Lin').
Denton	Type of differencing for Denton method: 'simple-diff', 'additive-first-diff', 'additive-second-diff', 'proportional-first-diff' and 'proportional-second-diff' (default is 'additive-first-diff'). For instance, 'simple-diff' differencing refers to the differences between the original and revised values, whereas 'additive-first-diff' differencing refers to the differences between the first differenced original and revised values.

Details

Takes in a n_l \times 1 low-frequency series to be disaggregated Y and a n \times p high-frequency matrix of p indicator series X. If n > n_l \times aggRatio where aggRatio is the aggregation ratio (e.g. aggRatio = 4 if annual-to-quarterly disagg, or aggRatio = 3 if quarterly-to-monthly disagg) then extrapolation is done to extrapolate up to n.

Value

y_Est: Estimated high-frequency response series (output is an n \times 1 matrix).

beta_Est: Estimated coefficient vector (output is a p \times 1 matrix).

rho_Est: Estimated residual AR(1) autocorrelation parameter.

ul_Est: Estimated aggregate residual series (output is an n_l \times 1 matrix).

References

\insertAllCited

Examples

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

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