Temporal disaggregation of a time series by model-based Denton proportional method

Denton proportional method can be expressed as a statistical model in a State space representation (see documentation for the definition of states). This approach is interesting as it allows more flexibility in the model such as the inclusion of outliers (level shift in the Benchmark to Indicator ratio) that could otherwise induce unintended wave effects with standard Denton method. Outliers and their intensity are defined by changing the value of the 'innovation variances'.

Usage

denton_modelbased(
  series,
  indicator,
  differencing = 1,
  conversion = c("Sum", "Average", "Last", "First", "UserDefined"),
  conversion.obsposition = 1,
  outliers = NULL,
  fixedBIratios = NULL
)

Arguments

series: Aggregation constraint. Mandatory. It must be either an object of class ts or a numeric vector.
indicator: High-frequency indicator. Mandatory. It must be of same class as series
differencing: Not implemented yet. Keep it equals to 1 (Denton PFD method).
conversion: Conversion rule. Usually "Sum" or "Average". Sum by default.
conversion.obsposition: Position of the observation in the aggregated period (only used with "UserDefined" conversion)
outliers: a list of structured definition of the outlier periods and their intensity. The period must be submitted first in the format YYYY-MM-DD and enclosed in quotation marks. This must be followed by an equal sign and the intensity of the outlier, defined as the relative value of the 'innovation variances' (1= normal situation)
fixedBIratios: a list of structured definition of the periods where the BI ratios must be fixed. The period must be submitted first in the format YYYY-MM-DD and enclosed in quotation marks. This must be followed by an equal sign and the value of the BI ratio.

Value

an object of class 'JD3MBDenton'

Examples

# retail data, monthly indicator
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1)
x<-rjd3toolkit::aggregate(rjd3toolkit::retail$FoodAndBeverageStores, 4)
td<-rjd3bench::denton_modelbased(Y, x, outliers = list("2000-01-01"=100, "2005-07-01"=100))
y<-td$estimation$edisagg

# qna data, quarterly indicator
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<-rjd3bench::denton_modelbased(Y, x)
td2<-rjd3bench::denton_modelbased(Y, x,
                                  outliers=list("2020-04-01"=100),
                                  fixedBIratios=list("2021-04-01"=39.0))

bi1<-td1$estimation$biratio
bi2<-td2$estimation$biratio
y1<-td1$estimation$disagg
y2<-td2$estimation$disagg
if (FALSE) { # \dontrun{
ts.plot(bi1,bi2,gpars=list(col=c("red","blue")))
ts.plot(y1,y2,gpars=list(col=c("red","blue")))
} # }