Temporal Disaggregation of a Time Series by ADL Model

Description

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

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

# 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

Calendarization

Description

Time series data do not always coincide with calendar periods (e.g., fiscal years starting in March-April or retail data collected in non-monthly intervals). Calendarization is the process of transforming the values of a flow time series observed over varying time intervals into values that cover given calendar intervals such as month, quarter or year. The process involves two steps. At first, a state-space representation of the Denton proportional first difference (PFD) method is considered to perform a temporal disaggregation of the observed data into daily values. After that, the resulting daily values are aggregated into the desired calendar reference periods.

Usage

calendarization(
  calendarobs,
  freq,
  start = NULL,
  end = NULL,
  dailyweights = NULL,
  stde = FALSE
)

Arguments

Value

A list containing the disaggregated daily values, the final aggregated series, and their associated standard errors if requested.

References

Quenneville, B., Picard F., Fortier S. (2012). Calendarization with interpolating splines and state space models. Statistics Canada, Appl. Statistics (2013) 62, part 3, pp 371-399.

See Also

For more information, see the vignette:

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

Examples

# Example 1 (from Quenneville et al (2012))

## Observed data
obs_1 <- list(
    list(start = "2009-02-18", end = "2009-03-17", value = 9000),
    list(start = "2009-03-18", end = "2009-04-14", value = 5000),
    list(start = "2009-04-15", end = "2009-05-12", value = 9500),
    list(start = "2009-05-13", end = "2009-06-09", value = 7000))

## a) calendarization in absence of daily indicator values (or weights)
cal_1a <- calendarization(obs_1, 12, end = "2009-06-30", dailyweights = NULL, stde = TRUE)

ym_1a <- cal_1a$rslt
eym_1a <- cal_1a$erslt
yd_1a <- cal_1a$days
eyd_1a <- cal_1a$edays

## b) calendarization in presence of daily indicator values (or weights)
x <- rep(c(1.0, 1.2, 1.8 , 1.6, 0.0, 0.6, 0.8), 19)
cal_1b <- calendarization(obs_1, 12, end = "2009-06-30", dailyweights = x, stde = TRUE)

ym_1b <- cal_1b$rslt
eym_1b <- cal_1b$erslt
yd_1b <- cal_1b$days
eyd_1b <- cal_1b$edays

# Example 2 (incl. negative value)

obs_2 <- 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_2 <- calendarization(obs_2, 4, end = "2003-12-31")

yq_2 <- cal_2$rslt

Benchmarking by means of the Cholette Method

Description

Cholette method is based on a benchmarking methodology developed at Statistics Canada. It is a generalized method relying on the principle of movement preservation that encompasses other benchmarking methods. The Denton method (both the AFD and PFD variants), as well as the naive pro-rating method, emerge as particular cases of the Cholette method. This method has been widely used for the purpose of benchmarking seasonally adjusted series among others.

Usage

cholette(
  s,
  t,
  rho = 1,
  lambda = 1,
  bias = c("None", "Additive", "Multiplicative"),
  conversion = c("Sum", "Average", "Last", "First", "UserDefined"),
  obsposition = 1L
)

Arguments

Value

A "ts" object with the benchmarked series

References

Quenneville, B., Fortier S., Chen Z.-G., Latendresse E. (2006). Recent Developments in Benchmarking to Annual Totals in X12-ARIMA and at Statistics Canada. Statistics Canada, Working paper of the Time Series Research and Analysis Centre.

See Also

For more information, see the vignette:

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

Examples

ym_true <- rjd3toolkit::Retail$RetailSalesTotal
yq_true <- rjd3toolkit::aggregate(ym_true, 4)
Y_full <- rjd3toolkit::aggregate(ym_true, 1)

Y <- window(Y_full, end = c(2009,1)) # say no benchmark yet for the year 2010
xm <- ym_true + rnorm(n = length(ym_true), mean = -5000, sd = 10000)
xq <- rjd3toolkit::aggregate(xm, 4)

# Proportional benchmarking with a bias and some recommended value of rho for
# monthly and quarterly series respectively
cholette(s = xm, t = Y, rho = 0.9, lambda = 1, bias = "Multiplicative")
cholette(s = xq, t = Y, rho = 0.729, lambda = 1, bias = "Multiplicative")

# Proportional benchmarking with no bias
xm_no_bias <- ym_true + rnorm(n = length(ym_true), mean = 0, sd = 10000)
cholette(s = xm_no_bias, t = Y, rho = 0.9, lambda = 1)

# Additive benchmarking
cholette(s = xm, t = Y, rho = 0.9, lambda = 0, bias = "Additive")

# Denton PFD
cholette(s = xm, t = Y, rho = 1, lambda = 1)

# Pro-rating
cholette(s = xm, t = Y, rho = 0, lambda = 0.5)

Benchmarking by means of Cubic Splines

Description

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 preliminary series is used, cubic splines are no longer drawn based on the low-frequency constraint 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 preliminary series.

Usage

cubicspline(
  s = NULL,
  t,
  nfreq = 4L,
  conversion = c("Sum", "Average", "Last", "First", "UserDefined"),
  obsposition = 1L
)

Arguments

Value

A "ts" object with the benchmarked series

See Also

For more information, see the vignette:

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

Examples

data("qna_data")
Y <- ts(qna_data$B1G_Y_data[,"B1G_FF"], frequency = 1, start = c(2009,1))

# Cubic spline without preliminary series
y1 <- cubicspline(t = Y, nfreq = 4L)

# Cubic spline with preliminary series
x1 <- y1 + rnorm(n = length(y1), mean = 0, sd = 10)
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))
cubicspline(s = x2, t = Y)

Benchmarking by means of the Denton Method.

Description

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.

Usage

denton(
  s = NULL,
  t,
  d = 1L,
  mul = TRUE,
  nfreq = 4L,
  modified = TRUE,
  conversion = c("Sum", "Average", "Last", "First", "UserDefined"),
  obsposition = 1L,
  nbcsts = 0L,
  nfcsts = 0L
)

Arguments

Value

A "ts" object with the benchmarked series

See Also

For more information, see the vignette:

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

Examples

Y <- rjd3toolkit::aggregate(rjd3toolkit::Retail$RetailSalesTotal, 1)

# Denton PFD without a preliminary series
y1 <- denton(t = Y, nfreq = 4)
print(y1)

# Denton PFD without a preliminary series and conversion = "Average"
denton(t = Y, nfreq = 4, conversion = "Average")

# Denton PFD with a preliminary series
x <- y1 + rnorm(n = length(y1), mean = 0, sd = 10000)
denton(s = x, t = Y)

# Denton AFD with a preliminary series
denton(s = x, t = Y, mul = FALSE)

Temporal Disaggregation and Interpolation of a Time Series using the Model-Based Denton Proportional Method

Description

The Denton proportional first difference (PFD) method can be expressed as a statistical model in a state-space representation. This formulation provides increased flexibility, including the ability to incorporate outliers, which correspond to level shifts in the Benchmark‑to‑Indicator (BI) ratio, that would otherwise induce unintended wave effects under the standard Denton PFD method. In addition, the approach allows the disaggregated series to be constrained (or 'frozen') at specific periods or prior to a given date by fixing the corresponding high‑frequency BI ratios.

Usage

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

Arguments

Value

an object of class 'JD3_MBDENTON_RSLTS' containing the results of the temporal disaggregation or interpolation procedure.

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$RetailSalesTotal, 1)
x <- rjd3toolkit::aggregate(rjd3toolkit::Retail$FoodAndBeverageStores, 4)
td <- 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 <- denton_modelbased(Y, x)
td2 <- 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

stats::ts.plot(bi2, bi1, main = "BI ratios",
               gpars = list(col = c("red", "black")))
graphics::legend("topright", lty = 1, col = c("black", "red"),
                 legend = c("td1", "td2"))
stats::ts.plot(y2, y1, main = "Disaggregated series",
               gpars = list(col = c("red", "black")))
graphics::legend("topleft", lty = 1, col = c("black", "red"),
                 legend = c("td1", "td2"))

Benchmarking of an Atypical Frequency Series by means of the Denton Method.

Description

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. The denton_raw() function extends denton() by allowing benchmarking for any frequency ratio.

Usage

denton_raw(
  s = NULL,
  t,
  freqratio,
  d = 1L,
  mul = TRUE,
  modified = TRUE,
  conversion = c("Sum", "Average", "Last", "First", "UserDefined"),
  obsposition = 1L,
  startoffset = 0L,
  nbcsts = 0L,
  nfcsts = 0L
)

Arguments

Value

A numeric vector with the benchmarked series.

See Also

For more information, see the vignette:

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

Examples

Y <- c(500,510,525,520)
x <- c(97, 98, 98.5, 99.5, 104,
       99, 100, 100.5, 101, 105.5,
       103, 104.5, 103.5, 104.5, 109,
       104, 107, 103, 108, 113,
       110)

# Denton PFD
# for example, x and Y could be annual and quinquennal series respectively
denton_raw(x, Y, freqratio = 5)

# Denton AFD
denton_raw(x, Y, freqratio = 5, mul = FALSE)

# Denton PFD without indicator
denton_raw(t = Y, freqratio = 2, conversion = "Average")

# Denton PFD with/without an offset and conversion = "Last"
x2 <- c(485,
        490, 492.5, 497.5, 520, 495,
        500, 502.5, 505, 527.5, 515,
        522.5, 517.5, 522.5, 545, 520,
        535, 515, 540, 565, 550)
denton_raw(x2, Y, freqratio = 5, conversion = "Last")
denton_raw(x2, Y, freqratio = 5, conversion = "Last", startoffset = 1)

Deprecated Functions

Description

This function is deprecated. Use the function temporal_disaggregation() or temporal_interpolation() instead.

Usage

temporaldisaggregation(
  series,
  constant = TRUE,
  trend = FALSE,
  indicators = NULL,
  model = c("Ar1", "Rw", "RwAr1"),
  freq = 4L,
  conversion = c("Sum", "Average", "Last", "First", "UserDefined"),
  conversion.obsposition = 1L,
  rho = 0,
  rho.fixed = FALSE,
  rho.truncated = 0,
  zeroinitialization = FALSE,
  diffuse.algorithm = c("SqrtDiffuse", "Diffuse", "Augmented"),
  diffuse.regressors = FALSE
)

Arguments

Value

Return the same value as either function that replaces it.

Benchmarking following the Growth Rate Preservation Principle.

Description

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.

Usage

grp(
  s,
  t,
  objective = c("Forward", "Backward", "Symmetric", "Log"),
  conversion = c("Sum", "Average", "Last", "First", "UserDefined"),
  obsposition = 1L,
  eps = 1e-12,
  iter = 500L,
  dentoninitialization = TRUE
)

Arguments

Value

A "ts" object with the benchmarked series

References

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.

Daalmans, J., Di Fonzo, T., Mushkudiani, N. and Bikker, R. (2018). Growth Rates Preservation (GRP) temporal benchmarking: Drawbacks and alternative solutions. Statistics Canada.

See Also

For more information, see the vignette:

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

Examples

data("qna_data")

Y <- ts(qna_data$B1G_Y_data[, "B1G_FF"], frequency = 1, start = c(2009, 1))
x <- denton(t = Y, nfreq = 4) + rnorm(n = length(Y) * 4, mean = 0, sd = 10)
grp(s = x, t = Y)

Reconciliation by means of the Multivariate Cholette Method

Description

This is a multivariate extension of the Cholette benchmarking method which can be used for the purpose of reconciliation. While standard benchmarking methods consider one target series at a time, reconciliation techniques aim to restore consistency in a system of time series with regards to both contemporaneous and temporal constraints. Reconciliation techniques are typically needed when the total and its components are estimated independently (the so-called direct approach). The multivariate Cholette method relies on the principle of movement preservation and encompasses other reconciliation methods such as the multivariate Denton method.

Usage

multivariatecholette(
  xlist,
  tcvector = NULL,
  ccvector = NULL,
  rho = 1,
  lambda = 0.8
)

Arguments

Value

A named list containing the benchmarked series

See Also

For more information, see the vignette:

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

Examples

# Example 1: one "standard" contemporaneous constraint: x1+x2+x3 = z

x1 <- ts(c(7, 7.2, 8.1, 7.5, 8.5, 7.8, 8.1, 8.4), frequency = 4, start = c(2010, 1))
x2 <- ts(c(18, 19.5, 19.0, 19.7, 18.5, 19.0, 20.3, 20.0), frequency = 4, start = c(2010, 1))
x3 <- ts(c(1.5, 1.8, 2, 2.5, 2.0, 1.5, 1.7, 2.0), frequency = 4, start = c(2010, 1))

z <- ts(c(27.1, 29.8, 29.9, 31.2, 29.3, 27.9, 30.9, 31.8), frequency = 4, start = c(2010, 1))

Y1 <- ts(c(30.0, 30.6), frequency = 1, start = c(2010, 1))
Y2 <- ts(c(80.0, 81.2), frequency = 1, start = c(2010, 1))
Y3 <- ts(c(8.0, 8.1), frequency = 1, start = c(2010, 1))

## Check consistency between temporal and contemporaneous constraints
lfs <- cbind(Y1,Y2,Y3)
rowSums(lfs) - stats::aggregate.ts(z) # should all be 0

data_list <- list(x1 = x1, x2 = x2, x3 = x3, z = z, Y1 = Y1, Y2 = Y2, Y3 = Y3)
tc <- c("Y1 = sum(x1)", "Y2 = sum(x2)", "Y3 = sum(x3)") # temporal constraints
cc <- c("z = x1+x2+x3") # (binding) contemporaneous constraint
cc_nb <- c("0 = x1+x2+x3-z") # non-binding contemporaneous constraint

## Run function with trade-off values for rho and lambda
multivariatecholette(xlist = data_list, tcvector = tc, ccvector = cc, rho = .5, lambda = .5)

## Run function with the value of rho corresponding to Denton or Cholette
multivariatecholette(xlist = data_list, tcvector = tc, ccvector = cc, rho = 1) # Denton
multivariatecholette(xlist = data_list, tcvector = tc, ccvector = cc, rho = 0.729) # Cholette

## Run function without temporal constraints
multivariatecholette(xlist = data_list, tcvector = NULL, ccvector = cc)

## Run function considering non-binding contemporaneous constraint
multivariatecholette(xlist = data_list, tcvector = tc, ccvector = cc_nb)

# Example 2: two contemporaneous constraints: x1+3*x2+0.5*x3+x4+x5 = z1 and x1+x2 = x4

x1 <- ts(c(7.0,7.3,8.1,7.5,8.5,7.8,8.1,8.4), frequency=4, start=c(2010,1))
x2 <- ts(c(1.5,1.8,2.0,2.5,2.0,1.5,1.7,2.0), frequency=4, start=c(2010,1))
x3 <- ts(c(18.0,19.5,19.0,19.7,18.5,19.0,20.3,20.0), frequency=4, start=c(2010,1))
x4 <- ts(c(8,9.5,9.0,10.7,8.5,10.0,10.3,9.0), frequency=4, start=c(2010,1))
x5 <- ts(c(5,9.6,7.2,7.1,4.3,4.6,5.3,5.9), frequency=4, start=c(2010,1))

z1 <- ts(c(38.1,41.8,41.9,43.2,38.8,39.1,41.9,43.7), frequency=4, start=c(2010,1))

Y1 <- ts(c(30.0,30.5), frequency=1, start=c(2010,1))
Y2 <- ts(c(10.0,10.5), frequency=1, start=c(2010,1))
Y3 <- ts(c(80.0,81.0), frequency=1, start=c(2010,1))
Y4 <- ts(c(40.0,41.0), frequency=1, start=c(2010,1))
Y5 <- ts(c(25.0,20.0), frequency=1, start=c(2010,1))

### check consistency between temporal and contemporaneous constraints
wlfs <- cbind(Y1,3*Y2,0.5*Y3,Y4,Y5)
rowSums(wlfs) - stats::aggregate.ts(z1) # cc1: should all be 0
Y1 + Y2 - Y4 # cc2: should all be 0

data.list <- list(x1=x1,x2=x2,x3=x3,x4=x4,x5=x5,z1=z1,Y1=Y1,Y2=Y2,Y3=Y3,Y4=Y4,Y5=Y5)
tc <- c("Y1=sum(x1)", "Y2=sum(x2)", "Y3=sum(x3)", "Y4=sum(x4)", "Y5=sum(x5)")
cc <- c("z1=x1+3*x2+0.5*x3+x4+x5", "0=x1+x2-x4")

multivariatecholette(xlist = data.list, tcvector = tc, ccvector = cc)

Quarterly National Accounts data for temporal disaggregation

Description

This dataset contains two data frames used for temporal disaggregation and benchmarking exercises. The first data frame, B1G_Y_data, includes three annual benchmark series corresponding to Belgian annual value added for the period 2009–2020 in three industries: chemical industry (CE), construction (FF), and transport services (HH). The second data frame, TURN_Q_data, contains the corresponding quarterly indicator series derived from VAT-based production indicators, covering the period 2009Q1–2021Q4.

Usage

qna_data

Format

A named list with two elements:

B1G_Y_data

A data frame with columns:

DATE

Annual periods.

B1G_CE

Value added for chemical industry.

B1G_FF

Value added for construction.

B1G_HH

Value added for transport services.

TURN_Q_data

A data frame with columns:

DATE

Quarterly periods.

TURN_INDEX_CE

Quarterly indicator for chemical industry.

TURN_INDEX_FF

Quarterly indicator for construction.

TURN_INDEX_HH

Quarterly indicator for transport services.

Source

Belgian Quarterly National Accounts

Examples

data(qna_data)
names(qna_data)
head(qna_data$B1G_Y_data)
head(qna_data$TURN_Q_data)

Temporal Disaggregation of a Time Series by Regression Models.

Description

Perform temporal disaggregation of low-frequency to high-frequency time series by regression models. The implemented models include Chow-Lin, Fernandez, Litterman and some variants of those algorithms.

Usage

temporal_disaggregation(
  series,
  constant = TRUE,
  trend = FALSE,
  indicators = NULL,
  model = c("Ar1", "Rw", "RwAr1"),
  freq = 4L,
  average = FALSE,
  rho = 0,
  rho.fixed = FALSE,
  rho.truncated = 0,
  zeroinitialization = FALSE,
  diffuse.algorithm = c("SqrtDiffuse", "Diffuse", "Augmented"),
  diffuse.regressors = FALSE,
  nbcsts = 0L,
  nfcsts = 0L
)

Arguments

Value

An object of class "JD3_TEMPDISAGG_RSLTS" containing the results of the temporal disaggregation procedure.

See Also

temporal_interpolation() for interpolation,

temporal_disaggregation_raw() for temporal disaggregation of atypical frequency series,

temporal_interpolation_raw() for interpolation of atypical frequency series

For more information, see the vignette:

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

Examples

# Chow-lin with a monthly indicator
Y <- rjd3toolkit::aggregate(rjd3toolkit::Retail$RetailSalesTotal, 1)
x <- rjd3toolkit::Retail$FoodAndBeverageStores
td <- temporal_disaggregation(Y, indicators = x)
td$estimation$disagg

# Fernandez with and without a 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 <- temporal_disaggregation(Y, indicators = x, model = "Rw")
td1$estimation$disagg

td2 <- temporal_disaggregation(Y, model = "Rw", nfcsts = 6)
td2$estimation$disagg

# Chow-lin applied to index series
Y_index <- 100 * Y / Y[1]
x_index <- 100 * x / x[1]
td3 <- temporal_disaggregation(Y, indicators = x, average = TRUE)
td3$estimation$disagg

Temporal Disaggregation of an Atypical Frequency Series by Regression Models.

Description

Perform temporal disaggregation of low-frequency to high-frequency time series by regression models. The implemented models include Chow-Lin, Fernandez, Litterman and some variants of those algorithms. The temporal_disaggregation_raw() function extends temporal_disaggregation() by allowing temporal disaggregation for any frequency ratio.

Usage

temporal_disaggregation_raw(
  series,
  constant = TRUE,
  trend = FALSE,
  indicators = NULL,
  startoffset = 0L,
  model = c("Ar1", "Rw", "RwAr1"),
  freqratio,
  average = FALSE,
  rho = 0,
  rho.fixed = FALSE,
  rho.truncated = 0,
  zeroinitialization = FALSE,
  diffuse.algorithm = c("SqrtDiffuse", "Diffuse", "Augmented"),
  diffuse.regressors = FALSE,
  nbcsts = 0L,
  nfcsts = 0L
)

Arguments

Value

An object of class "JD3_TEMPDISAGGRAW_RSLTS" containing the results of the temporal disaggregation procedure.

See Also

temporal_interpolation_raw()

For more information, see the vignette:

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

Examples

# Use of Chow-lin method to disaggregate a biennial series with an annual indicator
Y <- stats::aggregate(rjd3toolkit::Retail$RetailSalesTotal, 0.5)
x <- stats::aggregate(rjd3toolkit::Retail$FoodAndBeverageStores, 1)
td <- temporal_disaggregation_raw(as.numeric(Y), indicators = as.numeric(x), freqratio = 2)
td$estimation$disagg

# Use of Fernandez method to disaggregate a series without indicator
# considering a frequency ratio of 5 (for example, it could be a quinquennial
# series to disaggregate on an annual basis)
Y2 <- c(500,510,525,520)
td2 <- temporal_disaggregation_raw(Y2, model = "Rw", freqratio = 5, nfcsts = 2)
td2$estimation$disagg

# Same with an indicator, considering an offset in the latter
Y2 <- c(500,510,525,520)
x2 <- c(97,
        98, 98.5, 99.5, 104, 99,
        100, 100.5, 101, 105.5, 103,
        104.5, 103.5, 104.5, 109, 104,
        107, 103, 108, 113, 110)
td3 <- temporal_disaggregation_raw(Y2, indicators = x2, startoffset = 1,
                                   model = "Rw", freqratio = 5)
td3$estimation$disagg

Interpolation of a Time Series by Regression Models.

Description

Perform temporal interpolation of low-frequency to high-frequency time series by regression models. The implemented models include Chow-Lin, Fernandez, Litterman and some variants of those algorithms.

Usage

temporal_interpolation(
  series,
  constant = TRUE,
  trend = FALSE,
  indicators = NULL,
  model = c("Ar1", "Rw", "RwAr1"),
  freq = 4L,
  obsposition = -1L,
  rho = 0,
  rho.fixed = FALSE,
  rho.truncated = 0,
  zeroinitialization = FALSE,
  diffuse.algorithm = c("SqrtDiffuse", "Diffuse", "Augmented"),
  diffuse.regressors = FALSE,
  nbcsts = 0L,
  nfcsts = 0L
)

Arguments

Value

An object of class "JD3_INTERP_RSLTS" containing the results of the temporal interpolation procedure.

See Also

temporal_disaggregation(),

temporal_interpolation_raw() for interpolation of atypical frequency series,

temporal_disaggregation_raw() for temporal disaggregation of atypical frequency series

For more information, see the vignette:

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

Examples

# Chow-lin / Fernandez when the last value of the interpolated series is
# consistent with the low-frequency series
Y <- rjd3toolkit::aggregate(rjd3toolkit::Retail$RetailSalesTotal, 1)
x <- rjd3toolkit::Retail$FoodAndBeverageStores
ti1 <- temporal_interpolation(Y, indicators = x)
ti1$estimation$interp

ti2 <- temporal_interpolation(Y, indicators = x, model = "Rw")
ti2$estimation$interp

# Same without indicator
ti3 <- temporal_interpolation(Y, model = "Rw", freq = 12, nfcsts = 6)
ti3$estimation$interp

ti4 <- temporal_interpolation(Y, indicators = x, obsposition = 1)
ti4$estimation$interp

Interpolation of an Atypical Frequency Series by Regression Models.

Description

Perform temporal interpolation of low-frequency to high-frequency time series by regression models. The implemented models include Chow-Lin, Fernandez, Litterman and some variants of those algorithms. The temporal_interpolation_raw() function extends temporal_interpolation() by allowing temporal interpolation for any frequency ratio.

Usage

temporal_interpolation_raw(
  series,
  constant = TRUE,
  trend = FALSE,
  indicators = NULL,
  startoffset = 0L,
  model = c("Ar1", "Rw", "RwAr1"),
  freqratio,
  obsposition = -1L,
  rho = 0,
  rho.fixed = FALSE,
  rho.truncated = 0,
  zeroinitialization = FALSE,
  diffuse.algorithm = c("SqrtDiffuse", "Diffuse", "Augmented"),
  diffuse.regressors = FALSE,
  nbcsts = 0L,
  nfcsts = 0L
)

Arguments

Value

An object of class "JD3_INTERPRAW_RSLTS" containing the results of the temporal interpolation procedure.

See Also

temporal_disaggregation_raw()

For more information, see the vignette:

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

Examples

# Use of Chow-lin method to interpolate a biennial series with an annual
# indicator (the low-frequency series is consistent with the last value of the
# interpolated series)
Y <- stats::aggregate(rjd3toolkit::Retail$RetailSalesTotal, 0.5)
x <- stats::aggregate(rjd3toolkit::Retail$FoodAndBeverageStores, 1)
ti <- temporal_interpolation_raw(as.numeric(Y), indicators = as.numeric(x), freqratio = 2)
ti$estimation$interp

# Use of Fernandez method to interpolate a series without indicator
# considering a frequency ratio of 5 (the low-frequency series is consistent
# with the last value of the interpolated series). For example, Y2 could be a
# quinquennial series to interpolate annually.
Y2 <- c(500,510,525,520)
ti2 <- temporal_interpolation_raw(Y2, model = "Rw", freqratio = 5, nbcsts = 1, nfcsts = 2)
ti2$estimation$interp

# Same with an indicator, considering an offset in the latter
Y2 <- c(500,510,525,520)
x2 <- c(485,
        490, 492.5, 497.5, 520, 495,
        500, 502.5, 505, 527.5, 515,
        522.5, 517.5, 522.5, 545, 520,
        535, 515, 540, 565, 550)
ti3 <- temporal_interpolation_raw(Y2, indicators = x2, startoffset = 1, model = "Rw", freqratio = 5)
ti3$estimation$interp

# Same considering that the first value of the interpolated series is the one
# consistent with the low-frequency series
ti4 <- temporal_interpolation_raw(Y2, indicators = x2, startoffset = 1,
                                  model = "Rw", freqratio = 5, obsposition = 1)
ti4$estimation$interp

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

Description

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

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

# 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
td$regression$b