disaggregate: Temporal Disaggregation Methods

In TSdisaggregation: High-Dimensional Temporal Disaggregation

Temporal Disaggregation Methods

Description

This function contains the traditional standard-dimensional temporal disaggregation methods proposed by \insertCitedenton1971adjustment;textualTSdisaggregation, \insertCitedagum2006benchmarking;textualTSdisaggregation, \insertCitechow1971best;textualTSdisaggregation, \insertCitefernandez1981methodological;textualTSdisaggregation and \insertCitelitterman1983random;textualTSdisaggregation, and the high-dimensional methods of \insertCitemosley2021sparse;textualTSdisaggregation.

Usage

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

Arguments

Y	The low-frequency response series (n_l x 1 matrix).
X	The high-frequency indicator series (n x 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: 'absolute', 'first', 'second' and 'proportional' (default is 'first').

Details

Takes in a n_l x 1 low-frequency series to be disaggregated Y and a n x p high-frequency matrix of p indicator series X. If n > n_l x aggRatio where aggRatio is the aggregation ration (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 (n x 1 matrix).

beta_Est Estimated coefficient vector (p x 1 matrix).

rho_Est Estimated residual AR(1) autocorrelation parameter.

ul_Est Estimated aggregate residual series (n_l x 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

TSdisaggregation documentation built on May 19, 2022, 1:06 a.m.