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Temporal Disaggregation and Interpolation of a Time Series by means of a Reverse Regression Model.

Source: R/tempdisagg.R
temporaldisaggregationI.Rd

Perform temporal disaggregation and interpolation of low-frequency to high frequency time series by means of a reverse regression model. Unlike the usual regression-based models, this approach treats a high-frequency indicator as the dependent variable and the unknown target series as the independent variable.

Usage

temporaldisaggregationI(
  series,
  indicator,
  conversion = c("Sum", "Average", "Last", "First", "UserDefined"),
  conversion.obsposition = 1L,
  rho = 0,
  rho.fixed = FALSE,
  rho.truncated = 0
)

Arguments

series

A low-frequency time series to be disaggregated or interpolated. It must be a "ts" object.

indicator

A high-frequency indicator series. It must be a "ts" object.

conversion

A character string specifying the conversion mode, typically "Sum" or "Average" for disaggregation. The default is "Sum".

conversion.obsposition

An integer specifying the position of the low-frequency observations within the interpolated series (e.g. the 7th month of the year). This argument is used only for interpolation when conversion = "UserDefined".

rho

A numeric value giving the (initial) value of the autoregressive parameter.

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[.

Value

An object of class "JD3_TEMPDISAGGI_RSLTS" containing the results of the temporal disaggregation or interpolation procedure.

References

Bournay J., Laroque G. (1979). Reflexions sur la methode d'elaboration des comptes trimestriels. Annales de l'Insee, n. 36, pp.3-30.

See also

For more information, see the vignette:

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

Examples

# Retail data, monthly indicator
Y <- rjd3toolkit::aggregate(rjd3toolkit::Retail$RetailSalesTotal, 1)
x <- rjd3toolkit::Retail$FoodAndBeverageStores
td <- temporaldisaggregationI(Y, indicator = x)
td$estimation$disagg
#>           Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
#> 1992 126151.4 107410.9 128083.0 138396.0 165377.2 148824.2 179437.6 162492.2
#> 1993 138578.9 103853.3 147321.3 154540.9 174656.3 164929.5 196067.4 162563.4
#> 1994 143711.3 111338.0 174125.7 160660.8 179163.6 185013.2 198812.3 186296.7
#> 1995 157744.7 122590.1 182487.1 171984.8 197568.6 195306.7 203727.9 199054.0
#> 1996 168801.1 150593.4 192395.0 175203.8 215880.4 198191.9 215626.8 222696.2
#> 1997 188775.7 139600.5 211453.1 177757.2 231046.3 195106.7 229075.3 225673.2
#> 1998 195702.9 143675.5 193913.8 203268.2 235965.9 209780.0 246758.5 227308.7
#> 1999 201904.1 161675.4 222343.0 214726.9 250130.7 226500.0 266879.7 232348.2
#> 2000 200329.0 188374.8 243243.7 240487.6 264050.4 259951.0 271167.5 261848.0
#> 2001 218689.3 185385.5 252820.8 229993.8 278607.6 263469.7 266290.1 275438.1
#> 2002 239435.6 194734.5 274322.7 221790.5 291738.3 260931.5 280568.5 283542.4
#> 2003 258443.8 201870.3 259341.4 254237.1 300870.3 262128.4 300489.9 292429.7
#> 2004 277071.5 224932.4 269161.8 273311.7 310803.0 282978.2 320414.4 284596.1
#> 2005 279487.2 227552.6 306711.8 282930.1 319830.6 309543.4 331711.0 314943.2
#> 2006 281369.3 245794.0 311854.9 302747.5 345947.5 329893.0 344140.0 340467.3
#> 2007 308963.2 261435.7 337115.2 302853.0 364124.3 345379.8 346735.5 347791.7
#> 2008 315459.7 280849.6 332383.8 297965.3 374023.1 324658.0 359978.5 353838.4
#> 2009 304064.3 218875.8 275031.9 286571.0 337270.9 295391.4 330278.4 310191.8
#> 2010 295612.1 244320.7 316694.0 297897.3 346458.2 313042.9 348997.5 324778.6
#>           Sep      Oct      Nov      Dec
#> 1992 138640.0 162249.5 143931.0 214723.1
#> 1993 151499.7 162525.5 154638.2 231073.7
#> 1994 172389.3 172191.3 173629.2 252689.8
#> 1995 178460.4 171743.8 184036.5 257799.5
#> 1996 173486.7 197135.6 206172.9 250481.2
#> 1997 186432.7 212337.4 208126.7 268618.2
#> 1998 201331.8 225928.6 208958.5 294512.6
#> 1999 225827.4 234265.3 228935.9 343019.5
#> 2000 240853.8 235249.4 250610.0 332590.7
#> 2001 240754.2 251222.2 266005.5 339048.2
#> 2002 231047.3 256872.8 276188.9 323149.0
#> 2003 249837.0 275818.6 274172.2 338515.4
#> 2004 277356.2 290904.0 289442.8 379457.8
#> 2005 298932.0 306274.0 310994.2 407780.8
#> 2006 308481.4 316987.5 333050.8 419402.9
#> 2007 309502.6 321701.1 342018.5 418177.2
#> 2008 295511.5 324602.7 320836.1 372826.3
#> 2009 279697.2 310369.3 301353.7 389375.3
#> 2010 308656.4 331646.0 336461.7 424899.6

# qna data, quarterly indicator
data("qna_data")
Y <- ts(qna_data$B1G_Y_data[,"B1G_CE"], frequency = 1, start = c(2009,1))
x <- ts(qna_data$TURN_Q_data[,"TURN_INDEX_CE"], frequency = 4, start = c(2009,1))
td <- temporaldisaggregationI(Y, indicator = x)
td$regression$a
#> [1] 28.43446
td$regression$b
#> [1] 0.0303505