disaggregate {DisaggregateTS}R Documentation

Temporal Disaggregation Methods

Description

This 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).

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

Chow GC, Lin A (1971). “Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series.” The review of Economics and Statistics, 53(4), 372–375.

Dagum EB, Cholette PA (2006). Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series. Springer.

Denton FT (1971). “Adjustment of monthly or quarterly series to annual totals: an approach based on quadratic minimization.” Journal of the american statistical association, 66(333), 99–102.

Fernández RB (1981). “A methodological note on the estimation of time series.” The Review of Economics and Statistics, 63(3), 471–476.

Litterman RB (1983). “A random walk, Markov model for the distribution of time series.” Journal of Business & Economic Statistics, 1(2), 169–173.

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.

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

[Package DisaggregateTS version 3.0.1 Index]