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Compute quality criteria for asymmetric filters

Source: R/get_properties_function.R
diagnostic_matrix.Rd

Function du compute a diagnostic matrix of quality criteria for asymmetric filters

Usage

diagnostic_matrix(x, lags, passband = pi/6, sweights = NULL, ...)

Arguments

x

moving_average object or weights of the asymmetric filter (from -lags to m).

lags

Lags of the filter (should be positive), only used if x is not a moving_average.

passband

passband threshold.

sweights

Weights of the symmetric filter (from 0 to lags or -lags to lags). If missing, the criteria from the functions mse are not computed.

...

optional arguments to mse.

Details

For a moving average of coefficients \(\theta=(\theta_i)_{-p\le i\le q}\) diagnostic_matrix returns a list with the following ten criteria:

  • b_c Constant bias (if \(b_c=0\), \(\theta\) preserve constant trends) $$\sum_{i=-p}^q\theta_i - 1$$

  • b_l Linear bias (if \(b_c=b_l=0\), \(\theta\) preserve constant trends) $$\sum_{i=-p}^q i \theta_i$$

  • b_q Quadratic bias (if \(b_c=b_l=b_q=0\), \(\theta\) preserve quadratic trends) $$\sum_{i=-p}^q i^2 \theta_i$$

  • F_g Fidelity criterium of Grun-Rehomme et al (2018) $$$$

  • S_g Smoothness criterium of Grun-Rehomme et al (2018)

  • T_g Timeliness criterium of Grun-Rehomme et al (2018)

  • A_w Accuracy criterium of Wildi and McElroy (2019)

  • S_w Smoothness criterium of Wildi and McElroy (2019)

  • T_w Timeliness criterium of Wildi and McElroy (2019)

  • R_w Residual criterium of Wildi and McElroy (2019)

References

Grun-Rehomme, Michel, Fabien Guggemos, and Dominique Ladiray (2018). “Asymmetric Moving Averages Minimizing Phase Shift”. In: Handbook on Seasonal Adjustment.

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")
sweights <- filter[, "q=6"]
aweights <- filter[, "q=0"]
diagnostic_matrix(aweights, sweights = sweights)
#>           b_c           b_l           b_q           F_g           S_g 
#> -1.110223e-16 -4.066279e-01 -2.160733e+00  3.878572e-01  1.272295e+00 
#>           T_g           A_w           S_w           T_w           R_w 
#>  3.034079e-02  1.507927e-02  5.251704e-01  5.226739e-02  3.105944e-01