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Temporal Disaggregation of a Time Series by ADL Model

Source: R/adl.R
adl_disaggregation.Rd

Perform temporal disaggregation of low-frequency to high-frequency time series using an Autoregressive Distributed Lag regression model.

Usage

adl_disaggregation(
  series,
  constant = TRUE,
  trend = FALSE,
  indicators = NULL,
  average = FALSE,
  phi = 0,
  phi.fixed = FALSE,
  phi.truncated = 0,
  xar = c("FREE", "SAME", "NONE"),
  diffuse = FALSE
)

Arguments

series

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

constant

Boolean. Indicates whether a constant term is included in the model. The default is TRUE.

trend

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

indicators

One or more high-frequency indicator series. If not NULL (the default), this must be a "ts" object or a list of "ts" objects.

average

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

phi

A numeric value giving the (initial) value of the phi parameter

phi.fixed

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

phi.truncated

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

xar

A character string specifying the constraints imposed on the coefficients of the lagged regression variables. The default is "FREE", which indicates that no constraints are applied. For additional information, see the package vignette.

diffuse

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

Value

An object of class "JD3_ADLDISAGG_RSLTS" containing the results of the temporal disaggregation procedure.

References

Proietti, P. (2005). Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited. Working papers and Studies, European Commission, ISSN 1725-4825.

See also

For more information, see the vignette:

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

Examples

# ADL model
data("qna_data")
Y <- ts(qna_data$B1G_Y_data[,"B1G_FF"], frequency = 1, start = c(2009,1))
x <- ts(qna_data$TURN_Q_data[,"TURN_INDEX_FF"], frequency = 4, start = c(2009,1))
td1 <- adl_disaggregation(Y, indicators = x, xar = "FREE")
td1$estimation$disagg
#>          Qtr1     Qtr2     Qtr3     Qtr4
#> 2009 3942.777 4634.702 4135.613 4841.308
#> 2010 3741.857 4630.503 4105.827 5241.913
#> 2011 3904.498 4970.769 4345.346 5635.388
#> 2012 4155.180 5008.042 4449.625 5236.853
#> 2013 4163.930 4863.390 4389.478 5296.703
#> 2014 4359.020 4893.089 4403.275 5352.616
#> 2015 4224.764 5095.530 4516.377 5457.329
#> 2016 4320.028 5192.685 4620.448 5545.239
#> 2017 4554.219 5295.261 4566.921 5733.498
#> 2018 4745.362 5633.230 5077.478 6311.329
#> 2019 5045.944 5858.301 5197.201 6412.755
#> 2020 5042.060 4873.840 5147.902 6502.698
#> 2021 5332.868 6337.108 5427.407 6991.385

# ADL models with constraints
td2 <- adl_disaggregation(Y, indicators = x, xar = "SAME") # ~ Chow-Lin
td3 <- adl_disaggregation(Y, constant = FALSE, indicators = x,
                          xar = "SAME", phi = 1, phi.fixed = TRUE) # ~ Fernandez
td4 <- adl_disaggregation(Y, indicators = x, xar = "NONE") # ~ Santos Silva-Cardoso