| Title: | Interface to 'JDemetra+ 3.x' time series analysis software |
|---|---|
| Description: | R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. |
| Authors: | Jean Palate [aut], Corentin Lemasson [cre, ctb], Tanguy Barthelemy [ctb, art] |
| Maintainer: | Corentin Lemasson <[email protected]> |
| License: | EUPL | file LICENSE |
| Version: | 2.1.0.9000 |
| Built: | 2024-10-29 14:16:02 UTC |
| Source: | https://github.com/rjdverse/rjd3bench |
Temporal disaggregation of a time series with ADL models
adl_disaggregation( series, constant = TRUE, trend = FALSE, indicators = NULL, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), conversion.obsposition = 1, phi = 0, phi.fixed = FALSE, phi.truncated = 0, xar = c("FREE", "SAME", "NONE") )adl_disaggregation( series, constant = TRUE, trend = FALSE, indicators = NULL, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), conversion.obsposition = 1, phi = 0, phi.fixed = FALSE, phi.truncated = 0, xar = c("FREE", "SAME", "NONE") )
xar |
# qna data, fernandez with/without 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::adl_disaggregation(Y, indicators=x, xar="FREE") td2<-rjd3bench::adl_disaggregation(Y, indicators=x, xar="SAME")# qna data, fernandez with/without 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::adl_disaggregation(Y, indicators=x, xar="FREE") td2<-rjd3bench::adl_disaggregation(Y, indicators=x, xar="SAME")
Based on "Calendarization with splines and state space models" B. Quenneville, F.Picard and S.Fortier Appl. Statistics (2013) 62, part 3, pp 371-399. State space implementation.
calendarization( calendarobs, freq, start = NULL, end = NULL, dailyweights = NULL, stde = FALSE )calendarization( calendarobs, freq, start = NULL, end = NULL, dailyweights = NULL, stde = FALSE )
calendarobs |
Observations (list of start, end, value). See the example. |
freq |
Annual frequency. If 0, only the daily series are computed |
start |
Starting day of the calendarization. Could be before the calendar obs (extrapolation) |
end |
Final day of the calendarization. Could be after the calendar obs (extrapolation) |
dailyweights |
Daily weights. Should have the same length as the requested series |
stde |
obs<-list( list(start="1980-01-01", end="1989-12-31", value=100), list(start="1990-01-01", end="1999-12-31", value=-10), list(start="2000-01-01", end="2002-12-31", value=50)) cal<-calendarization(obs, 4, end="2003-12-31", stde=TRUE) Q<-cal$rslt eQ<-cal$ersltobs<-list( list(start="1980-01-01", end="1989-12-31", value=100), list(start="1990-01-01", end="1999-12-31", value=-10), list(start="2000-01-01", end="2002-12-31", value=50)) cal<-calendarization(obs, 4, end="2003-12-31", stde=TRUE) Q<-cal$rslt eQ<-cal$erslt
Benchmarking by means of the Cholette method.
cholette( s, t, rho = 1, lambda = 1, bias = "None", conversion = "Sum", obsposition = 1 )cholette( s, t, rho = 1, lambda = 1, bias = "None", conversion = "Sum", obsposition = 1 )
s |
Disaggregated series. Mandatory |
t |
Aggregation constraint. Mandatory |
obsposition |
Postion of the observation in the aggregated period (only used with "UserDefined" conversion) |
Cubic splines are piecewise cubic functions that are linked together in a way to guarantee smoothness at data points. Additivity constraints are added for benchmarking purpose and sub-period estimates are derived from each spline. When a sub-period indicator (or disaggregated series) is used, cubic splines are no longer drawn based on the low frequency data but the Benchmark-to-Indicator (BI ratio) is the one being smoothed. Sub- period estimates are then simply the product between the smoothed high frequency BI ratio and the indicator.
cubicspline( s = NULL, t, nfreq = 4, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), obsposition = 1 )cubicspline( s = NULL, t, nfreq = 4, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), obsposition = 1 )
s |
Disaggregated series. If not NULL, it must be the same class as t. |
t |
Aggregation constraint. Mandatory. it must be either an object of class ts or a numeric vector. |
nfreq |
Annual frequency of the disaggregated variable. Used if no disaggregated series is provided. |
conversion |
Conversion rule. Usually "Sum" or "Average". Sum by default. |
obsposition |
Postion of the observation in the aggregated period (only used with "UserDefined" conversion) |
The benchmarked series is returned
data("qna_data") Y<-ts(qna_data$B1G_Y_data[,"B1G_FF"], frequency=1, start=c(2009,1)) # cubic spline without disaggregated series y1<-rjd3bench::cubicspline(t=Y, nfreq=4) # cubic spline with disaggregated series x1<-y1+rnorm(n=length(y1), mean=0, sd=10) y2<-rjd3bench::cubicspline(s=x1, t=Y) # cubic splines used for temporal disaggregation x2<-ts(qna_data$TURN_Q_data[,"TURN_INDEX_FF"], frequency=4, start=c(2009,1)) y3<-rjd3bench::cubicspline(s=x2, t=Y)data("qna_data") Y<-ts(qna_data$B1G_Y_data[,"B1G_FF"], frequency=1, start=c(2009,1)) # cubic spline without disaggregated series y1<-rjd3bench::cubicspline(t=Y, nfreq=4) # cubic spline with disaggregated series x1<-y1+rnorm(n=length(y1), mean=0, sd=10) y2<-rjd3bench::cubicspline(s=x1, t=Y) # cubic splines used for temporal disaggregation x2<-ts(qna_data$TURN_Q_data[,"TURN_INDEX_FF"], frequency=4, start=c(2009,1)) y3<-rjd3bench::cubicspline(s=x2, t=Y)
Denton method relies on the principle of movement preservation. There exist a few variants corresponding to different definitions of movement preservation: additive first difference (AFD), proportional first difference (PFD), additive second difference (ASD), proportional second difference (PSD), etc. The default and most widely used is the Denton PFD method.
denton( s = NULL, t, d = 1, mul = TRUE, nfreq = 4, modified = TRUE, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), obsposition = 1 )denton( s = NULL, t, d = 1, mul = TRUE, nfreq = 4, modified = TRUE, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), obsposition = 1 )
s |
Preliminary series. If not NULL, it must be the same class as t. |
t |
Aggregation constraint. Mandatory. it must be either an object of class ts or a numeric vector. |
d |
Differencing order. 1 by default. |
mul |
Multiplicative or additive benchmarking. Multiplicative by default. |
nfreq |
Annual frequency of the disaggregated variable. Used if no disaggregated series is provided. |
modified |
Modified (TRUE) or unmodified (FALSE) Denton. Modified by default. |
conversion |
Conversion rule. Usually "Sum" or "Average". Sum by default. |
obsposition |
Position of the observation in the aggregated period (only used with "UserDefined" conversion). |
The benchmarked series is returned
Y <- ts(qna_data$B1G_Y_data$B1G_FF, frequency=1, start=c(2009,1)) # denton PFD without high frequency series y1 <- rjd3bench::denton(t=Y, nfreq=4) # denton PFD with high frequency series x <- y1 + rnorm(n=length(y1), mean=0, sd=10) y2 <- rjd3bench::denton(s=x, t=Y) # denton ASD y3 <- rjd3bench::denton(s=x, t=Y, d=2, mul=FALSE)Y <- ts(qna_data$B1G_Y_data$B1G_FF, frequency=1, start=c(2009,1)) # denton PFD without high frequency series y1 <- rjd3bench::denton(t=Y, nfreq=4) # denton PFD with high frequency series x <- y1 + rnorm(n=length(y1), mean=0, sd=10) y2 <- rjd3bench::denton(s=x, t=Y) # denton ASD y3 <- rjd3bench::denton(s=x, t=Y, d=2, mul=FALSE)
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'.
denton_modelbased( series, indicator, differencing = 1, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), conversion.obsposition = 1, outliers = NULL, fixedBIratios = NULL )denton_modelbased( series, indicator, differencing = 1, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), conversion.obsposition = 1, outliers = NULL, fixedBIratios = NULL )
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. |
an object of class 'JD3MBDenton'
# 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 ## Not run: ts.plot(bi1,bi2,gpars=list(col=c("red","blue"))) ts.plot(y1,y2,gpars=list(col=c("red","blue"))) ## End(Not run)# 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 ## Not run: ts.plot(bi1,bi2,gpars=list(col=c("red","blue"))) ts.plot(y1,y2,gpars=list(col=c("red","blue"))) ## End(Not run)
GRP is a method which explicitly preserves the period-to-period growth rates of the preliminary series. It corresponds to the method of Cauley and Trager (1981), using the solution proposed by Di Fonzo and Marini (2011). BFGS is used as line-search algorithm for the reduced unconstrained minimization problem.
grp( s, t, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), obsposition = 1, eps = 1e-12, iter = 500, dentoninitialization = TRUE )grp( s, t, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), obsposition = 1, eps = 1e-12, iter = 500, dentoninitialization = TRUE )
s |
Preliminary series. Mandatory. It must be a ts object. |
t |
Aggregation constraint. Mandatory. It must be a ts object. |
conversion |
Conversion rule. "Sum" by default. |
obsposition |
Position of the observation in the aggregated period (only used with "UserDefined" conversion) |
eps |
Numeric. Defines the convergence precision. BFGS algorithm is run until the reduction in the objective is within this eps value (1e-12 is the default) or until the maximum number of iterations is hit. |
iter |
Integer. Maximum number of iterations in BFGS algorithm (500 is the default). |
dentoninitialization |
indicate whether the series benchmarked via modified Denton PFD is used as starting values of the GRP optimization procedure (TRUE/FALSE, TRUE by default). If FALSE, the average benchmark is used for flow variables (e.g. t/4 for quarterly series with annual constraints and conversion = 'Sum'), or the benchmark for stock variables. |
The benchmarked series is returned
Causey, B., and Trager, M.L. (1981). Derivation of Solution to the Benchmarking Problem: Trend Revision. Unpublished research notes, U.S. Census Bureau, Washington D.C. Available as an appendix in Bozik and Otto (1988).
Di Fonzo, T., and Marini, M. (2011). A Newton's Method for Benchmarking Time Series according to a Growth Rates Preservation Principle. *IMF WP/11/179*.
data("qna_data") Y <- ts(qna_data$B1G_Y_data[,"B1G_FF"], frequency=1, start=c(2009,1)) x <- rjd3bench::denton(t=Y, nfreq=4) + rnorm(n=length(Y)*4, mean=0, sd=10) y_grp <- rjd3bench::grp(s=x, t=Y)data("qna_data") Y <- ts(qna_data$B1G_Y_data[,"B1G_FF"], frequency=1, start=c(2009,1)) x <- rjd3bench::denton(t=Y, nfreq=4) + rnorm(n=length(Y)*4, mean=0, sd=10) y_grp <- rjd3bench::grp(s=x, t=Y)
Multi-variate Cholette
multivariatecholette( xlist, tcvector = NULL, ccvector = NULL, rho = 1, lambda = 1 )multivariatecholette( xlist, tcvector = NULL, ccvector = NULL, rho = 1, lambda = 1 )
lambda |
Plot function for object of class JD3AdlDisagg
## S3 method for class 'JD3AdlDisagg' plot(x, ...)## S3 method for class 'JD3AdlDisagg' plot(x, ...)
x |
an object of class JD3AdlDisagg |
... |
further arguments to pass to ts.plot. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::adl_disaggregation(Y, indicator=x, xar="FREE") plot(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::adl_disaggregation(Y, indicator=x, xar="FREE") plot(td)
Plot function for object of class JD3MBDenton
## S3 method for class 'JD3MBDenton' plot(x, ...)## S3 method for class 'JD3MBDenton' plot(x, ...)
x |
an object of class JD3MBDenton |
... |
further arguments to pass to ts.plot. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) plot(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) plot(td)
Plot function for object of class JD3TempDisagg
## S3 method for class 'JD3TempDisagg' plot(x, ...)## S3 method for class 'JD3TempDisagg' plot(x, ...)
x |
an object of class JD3TempDisagg |
... |
further arguments to pass to ts.plot. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregation(Y, indicator=x) plot(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregation(Y, indicator=x) plot(td)
Plot function for object of class JD3TempDisaggI
## S3 method for class 'JD3TempDisaggI' plot(x, ...)## S3 method for class 'JD3TempDisaggI' plot(x, ...)
x |
an object of class JD3TempDisaggI |
... |
further arguments to pass to ts.plot. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) plot(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) plot(td)
Print function for object of class JD3AdlDisagg
## S3 method for class 'JD3AdlDisagg' print(x, ...)## S3 method for class 'JD3AdlDisagg' print(x, ...)
x |
an object of class JD3AdlDisagg |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::adl_disaggregation(Y, indicator=x, xar="FREE") print(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::adl_disaggregation(Y, indicator=x, xar="FREE") print(td)
Print function for object of class JD3MBDenton
## S3 method for class 'JD3MBDenton' print(x, ...)## S3 method for class 'JD3MBDenton' print(x, ...)
x |
an object of class JD3MBDenton |
... |
further arguments passed to or from other methods. |
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)) print(td)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)) print(td)
Print function for object of class JD3TempDisagg
## S3 method for class 'JD3TempDisagg' print(x, ...)## S3 method for class 'JD3TempDisagg' print(x, ...)
x |
an object of class JD3TempDisagg |
... |
further arguments passed to or from other methods. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregation(Y, indicator=x) print(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregation(Y, indicator=x) print(td)
Print function for object of class JD3TempDisaggI
## S3 method for class 'JD3TempDisaggI' print(x, ...)## S3 method for class 'JD3TempDisaggI' print(x, ...)
x |
an object of class JD3TempDisaggI |
... |
further arguments passed to or from other methods. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) print(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) print(td)
Summary function for object of class JD3AdlDisagg
## S3 method for class 'JD3AdlDisagg' summary(object, ...)## S3 method for class 'JD3AdlDisagg' summary(object, ...)
object |
an object of class JD3AdlDisagg |
... |
further arguments passed to or from other methods. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::adl_disaggregation(Y, indicator=x) summary(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::adl_disaggregation(Y, indicator=x) summary(td)
Summary function for object of class JD3MBDenton
## S3 method for class 'JD3MBDenton' summary(object, ...)## S3 method for class 'JD3MBDenton' summary(object, ...)
object |
an object of class JD3MBDenton |
... |
further arguments passed to or from other methods. |
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)) summary(td)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)) summary(td)
Summary function for object of class JD3TempDisagg
## S3 method for class 'JD3TempDisagg' summary(object, ...)## S3 method for class 'JD3TempDisagg' summary(object, ...)
object |
an object of class JD3TempDisagg |
... |
further arguments passed to or from other methods. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregation(Y, indicator=x) summary(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregation(Y, indicator=x) summary(td)
Summary function for object of class JD3TempDisaggI
## S3 method for class 'JD3TempDisaggI' summary(object, ...)## S3 method for class 'JD3TempDisaggI' summary(object, ...)
object |
an object of class JD3TempDisaggI |
... |
further arguments passed to or from other methods. |
Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) summary(td)Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) summary(td)
Perform 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.
temporaldisaggregation( series, constant = TRUE, trend = FALSE, indicators = NULL, model = c("Ar1", "Rw", "RwAr1"), freq = 4, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), conversion.obsposition = 1, rho = 0, rho.fixed = FALSE, rho.truncated = 0, zeroinitialization = FALSE, diffuse.algorithm = c("SqrtDiffuse", "Diffuse", "Augmented"), diffuse.regressors = FALSE )temporaldisaggregation( series, constant = TRUE, trend = FALSE, indicators = NULL, model = c("Ar1", "Rw", "RwAr1"), freq = 4, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), conversion.obsposition = 1, rho = 0, rho.fixed = FALSE, rho.truncated = 0, zeroinitialization = FALSE, diffuse.algorithm = c("SqrtDiffuse", "Diffuse", "Augmented"), diffuse.regressors = FALSE )
series |
The time series that will be disaggregated. It must be a ts object. |
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. It must be a (list of) ts object(s). |
model |
Model of the error term (at the disaggregated level). "Ar1" = Chow-Lin, "Rw" = Fernandez, "RwAr1" = Litterman |
freq |
Annual frequency of the disaggregated variable. Used if no indicator is provided |
conversion |
Conversion mode (Usually "Sum" or "Average") |
conversion.obsposition |
Only used with "UserDefined" mode. Position of the observed indicator in the aggregated periods (for instance 7th month of the year) |
rho |
Only used with Ar1/RwAr1 models. (Initial) value of the parameter |
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) |
An object of class "JD3TempDisagg"
# retail data, chow-lin with monthly indicator Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregation(Y, indicators=x) y<-td$estimation$disagg # qna data, fernandez with/without 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::temporaldisaggregation(Y, indicators=x, model = "Rw") td2<-rjd3bench::temporaldisaggregation(Y, model = "Rw") mod1<- td1$regression$model# retail data, chow-lin with monthly indicator Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregation(Y, indicators=x) y<-td$estimation$disagg # qna data, fernandez with/without 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::temporaldisaggregation(Y, indicators=x, model = "Rw") td2<-rjd3bench::temporaldisaggregation(Y, model = "Rw") mod1<- td1$regression$model
Temporal disaggregation using the model: x(t) = a + b y(t), where x(t) is the indicator, y(t) is the unknown target series, with low-frequency constraints on y.
temporaldisaggregationI( series, indicator, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), conversion.obsposition = 1, rho = 0, rho.fixed = FALSE, rho.truncated = 0 )temporaldisaggregationI( series, indicator, conversion = c("Sum", "Average", "Last", "First", "UserDefined"), conversion.obsposition = 1, rho = 0, rho.fixed = FALSE, rho.truncated = 0 )
series |
The time series that will be disaggregated. It must be a ts object. |
indicator |
High-frequency indicator used in the temporal disaggregation. It must be a ts object. |
conversion |
Conversion mode (Usually "Sum" or "Average") |
conversion.obsposition |
Only used with "UserDefined" mode. Position of the observed indicator in the aggregated periods (for instance 7th month of the year) |
rho |
Only used with Ar1/RwAr1 models. (Initial) value of the parameter |
rho.fixed |
Fixed rho (T/F, F by default) |
rho.truncated |
Range for Rho evaluation (in [rho.truncated, 1[) |
An object of class "JD3TempDisaggI"
# retail data, monthly indicator Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) y<-td$estimation$disagg # 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<-rjd3bench::temporaldisaggregationI(Y, indicator=x) a<-td$regression$a b<-td$regression$b# retail data, monthly indicator Y<-rjd3toolkit::aggregate(rjd3toolkit::retail$RetailSalesTotal, 1) x<-rjd3toolkit::retail$FoodAndBeverageStores td<-rjd3bench::temporaldisaggregationI(Y, indicator=x) y<-td$estimation$disagg # 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<-rjd3bench::temporaldisaggregationI(Y, indicator=x) a<-td$regression$a b<-td$regression$b