Accuracy/smoothness/timeliness criteria through spectral decomposition

Usage

mse(aweights, sweights, density = c("uniform", "rw"), passband = pi/6, ...)

Arguments

aweights: moving_average object or weights of the asymmetric filter (from -n to m).
sweights: moving_average object or weights of the symmetric filter (from 0 to n or -n to n).
density: hypothesis on the spectral density: "uniform" (= white woise, the default) or "rw" (= random walk).
passband: passband threshold.
...: other unused arguments.

Value

The criteria

References

Wildi, Marc and McElroy, Tucker (2019). “The trilemma between accuracy, timeliness and smoothness in real-time signal extraction”. In: International Journal of Forecasting 35.3, pp. 1072–1084.

Examples

filter <- lp_filter(horizon = 6, kernel = "Henderson", endpoints = "LC")
#> Error in .jcall("jdplus/filters/base/r/LocalPolynomialFilters", "Ljdplus/toolkit/base/core/math/linearfilters/ISymmetricFiltering;",     "filters", as.integer(horizon), as.integer(degree), kernel,     endpoints, d, tweight, passband): RcallMethod: cannot determine object class
sweights <- filter[, "q=6"]
#> Error in filter[, "q=6"]: object of type 'closure' is not subsettable
aweights <- filter[, "q=0"]
#> Error in filter[, "q=0"]: object of type 'closure' is not subsettable
mse(aweights, sweights)
#> Error: object 'aweights' not found
# Or to compute directly the criteria on all asymmetric filters:
mse(filter)
#> Error in mse.default(filter): argument "sweights" is missing, with no default