Skip to contents

Benchmarking following the growth rate preservation principle.

Source: R/benchmark.R
grp.Rd

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

s

Preliminary series. Mandatory. It must be a ts object.

t

Aggregation constraint. Mandatory. It must be a ts object.

objective

Objective function. See vignette and/or Daalmans et al. (2018) for more information.

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.

Value

The benchmarked series is returned

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 Unpublished research notes, U.S. Census Bureau, Washington D.C. Available as an appendix in Bozik and Otto (1988).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*.

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)
#>          Qtr1     Qtr2     Qtr3     Qtr4
#> 2009 4401.435 4401.478 4368.816 4382.670
#> 2010 4367.369 4394.138 4436.358 4522.235
#> 2011 4624.236 4701.365 4768.325 4762.073
#> 2012 4742.554 4717.748 4698.807 4690.591
#> 2013 4674.068 4677.785 4687.841 4673.805
#> 2014 4716.244 4740.478 4758.178 4793.100
#> 2015 4787.351 4803.442 4830.292 4872.915
#> 2016 4891.612 4926.765 4930.383 4929.640
#> 2017 4944.282 4991.178 5053.669 5160.771
#> 2018 5305.466 5395.682 5505.310 5560.942
#> 2019 5645.579 5639.614 5645.524 5583.483
#> 2020 5470.927 5395.838 5357.786 5341.949