Temporal disaggregation of an atypical frequency series by regression models.
Source:R/tempdisagg.R
temporal_disaggregation_raw.RdPerform temporal disaggregation of low frequency to high frequency time series by regression models. Models included are Chow-Lin, Fernandez, Litterman and some variants of those algorithms. This "raw" function extends the temporal_disaggregation() function in a way that it can deal with 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
The low frequency series that will be disaggregated. Must be a numeric vector.
- constant
Constant term (T/F). Only used with "Ar1" model when zeroinitialization = F.
- trend
Linear trend (T/F)
- indicators
High-frequency indicator(s) used in the temporal disaggregation. If not NULL, it must be either a numeric vector or a matrix.
- startoffset
Number of initial observations in the indicator(s) series that are prior to the start of the period covered by the low-frequency series. Must be 0 or a positive integer. 0 by default. Ignored when no indicator is provided.
- model
Model of the error term (at the disaggregated level). "Ar1" = Chow-Lin, "Rw" = Fernandez, "RwAr1" = Litterman.
- freqratio
Frequency ratio between the disaggregated series and the low frequency series. Mandatory. Must be a positive integer.
- average
Average conversion (T/F). Default is F, which means additive conversion.
- rho
(Initial) value of the parameter. Only used with Ar1/RwAr1 models.
- rho.fixed
Fixed rho (T/F, F by default)
- rho.truncated
Range for Rho evaluation (in [rho.truncated, 1[)
- zeroinitialization
The initial values of an auto-regressive model are fixed to 0 (T/F, F by default)
- diffuse.algorithm
Algorithm used for diffuse initialization. "SqrtDiffuse" by default
- diffuse.regressors
Indicates if the coefficients of the regression model are diffuse (T) or fixed unknown (F, default)
- nbcsts
Number of backcast periods. Ignored when an indicator is provided.
- nfcsts
Number of forecast periods. Ignored when an indicator is provided.
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] 1853647 1904317 2106162 2226363 2362164 2478504 2596417 2799244 2935728
#> [10] 3120753 3141809 3260667 3478646 3698475 3902189 3983745 3853107 3738297
#> [19] 3973628
# 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