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Temporal disaggregation of a time series with ADL models

Source: R/adl.R
adl_disaggregation.Rd

Temporal disaggregation of a time series with ADL models

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

The low frequency time series that will be disaggregated. It must be a ts object.

constant

Constant term (T/F, T by default)

trend

Linear trend (T/F, F by default)

indicators

High-frequency indicator(s). It must be a (list of) ts object(s).

average

Average conversion (T/F). Default is F, which means additive conversion.

phi

(Initial) value of the phi parameter

phi.fixed

Fixed phi (T/F, F by default)

phi.truncated

Range for phi evaluation (in [phi.truncated, 1[)

xar

Constraints on the coefficients of the lagged regression variables. See vignette for more information on this.

diffuse

Indicates if the coefficients of the regression model are diffuse (T) or fixed unknown (F, default)

Value

An object of class "JD3AdlDisagg"

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:

browseVignettes 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