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Temporal Disaggregation of an Atypical Frequency Series by Regression Models.

Source: R/tempdisagg.R
temporal_disaggregation_raw.Rd

Perform temporal disaggregation of low-frequency to high-frequency time series by regression models. The implemented models include Chow-Lin, Fernandez, Litterman and some variants of those algorithms. The temporal_disaggregation_raw() function extends temporal_disaggregation() by allowing temporal disaggregation for any frequency ratio.

Usage

temporal_disaggregation_raw(
  series,
  constant = TRUE,
  trend = FALSE,
  indicators = NULL,
  startoffset = 0L,
  model = c("Ar1", "Rw", "RwAr1"),
  freqratio,
  average = FALSE,
  rho = 0,
  rho.fixed = FALSE,
  rho.truncated = 0,
  zeroinitialization = FALSE,
  diffuse.algorithm = c("SqrtDiffuse", "Diffuse", "Augmented"),
  diffuse.regressors = FALSE,
  nbcsts = 0L,
  nfcsts = 0L
)

Arguments

series

A low-frequency time series to be disaggregated. It must be a numeric vector.

constant

Boolean. Indicates whether a constant term is included in the model. The default is TRUE. Note that this argument is used only with model = "Ar1" when zeroinitialization = FALSE. For additional information on this, see the package vignette.

trend

Boolean. Indicates whether a linear trend is included in the model. The default is FALSE.

indicators

One or more high-frequency indicator series used in the temporal disaggregation. If NULL (the default), no indicator is used. When provided, the argument must be a numeric vector or a matrix.

startoffset

The number of initial observations in the indicator series that precede the start of the low-frequency series. The value must be either 0 or a positive integer (default is 0). This argument is ignored when no indicator is provided.

model

A character string specifying the model of the error term at the disaggregated level. The options are: "Ar1" (Chow Lin), "Rw" (Fernandez), and "RwAr1" (Litterman).

freqratio

An integer specifying the frequency ratio between the disaggregated series and the low-frequency series. This argument is mandatory and must be a positive integer.

average

Boolean. Indicates whether an average conversion should be considered. The default is FALSE, corresponding to additive conversion.

rho

A numeric value giving the (initial) value of the autoregressive parameter. This argument is used only for "Ar1" and "RwAr1" models.

rho.fixed

Boolean. Specifies whether the supplied value of rho is fixed. The default is FALSE, which indicates that rho is estimated.

rho.truncated

A numeric value defining the lower bound of the admissible range for rho. The evaluation range is [rho.truncated, 1[.

zeroinitialization

Boolean. If TRUE, the initial values of the autoregressive model are set to zero. The default is FALSE.

diffuse.algorithm

A character string specifying the algorithm used for diffuse initialization. The default is "SqrtDiffuse".

diffuse.regressors

Boolean. Indicates whether the coefficients of the regression model are treated as diffuse (TRUE) or as fixed unknown (FALSE, the default).

nbcsts

An integer specifying the number of backcast periods. This argument is ignored when one or more indicator series is provided.

nfcsts

An integer specifying the number of forecast periods. This argument is ignored when one or more indicator series is provided.

Value

An object of class "JD3_TEMPDISAGGRAW_RSLTS" containing the results of the temporal disaggregation procedure.

See also

temporal_interpolation_raw()

For more information, see the vignette:

utils::browseVignettes(), e.g. browseVignettes(package = "rjd3bench")

Examples

# Use of Chow-lin method to disaggregate a biennial series with an annual indicator
Y <- stats::aggregate(rjd3toolkit::Retail$RetailSalesTotal, 0.5)
x <- stats::aggregate(rjd3toolkit::Retail$FoodAndBeverageStores, 1)
td <- temporal_disaggregation_raw(as.numeric(Y), indicators = as.numeric(x), freqratio = 2)
td$estimation$disagg
#>  [1] 1853646 1904318 2106162 2226363 2362164 2478504 2596417 2799244 2935728
#> [10] 3120753 3141809 3260667 3478645 3698476 3902189 3983745 3853109 3738295
#> [19] 3973614

# Use of Fernandez method to disaggregate a series without indicator
# considering a frequency ratio of 5 (for example, it could be a quinquennial
# series to disaggregate on an annual basis)
Y2 <- c(500,510,525,520)
td2 <- temporal_disaggregation_raw(Y2, model = "Rw", freqratio = 5, nfcsts = 2)
td2$estimation$disagg
#>  [1]  99.70153  99.77615  99.92538 100.14923 100.44770 100.82078 101.30213
#>  [8] 101.89174 102.58961 103.39574 104.31014 104.93980 105.28473 105.34493
#> [15] 105.12039 104.61112 104.20371 103.89815 103.69444 103.59258 103.59258
#> [22] 103.59258

# Same with an indicator, considering an offset in the latter
Y2 <- c(500,510,525,520)
x2 <- c(97,
        98, 98.5, 99.5, 104, 99,
        100, 100.5, 101, 105.5, 103,
        104.5, 103.5, 104.5, 109, 104,
        107, 103, 108, 113, 110)
td3 <- temporal_disaggregation_raw(Y2, indicators = x2, startoffset = 1,
                                   model = "Rw", freqratio = 5)
td3$estimation$disagg
#>  [1]  98.77697  99.16257  99.39121  99.84848 101.69120  99.90653 100.47131
#>  [8] 100.94308 101.51462 103.72835 103.34265 104.59913 104.59495 105.06531
#> [15] 106.58860 104.15201 104.50357 102.31697 103.76183 105.36774 104.04989