dfa_analytic: This function computes MSE (mean-square) DFA-solutions as...

In wiaidp/MDFA: Signalextraction and Forecasting Based on the Multivariate Direct Filter Approach (MDFA)

Description

This function computes MSE (mean-square) DFA-solutions as well as customized filters: it replicates the function dfa_ms if lambda=eta=0 and if i1=i2=F. It can solve univariate signal extraction problems only. It can handle constraints. Regularization is not possible.

Usage

dfa_analytic(L, lambda, periodogram, Lag, Gamma, eta, cutoff, i1, i2)

Arguments

L	Filter length
lambda	Customization parameter: Timeliness is emphasized in the ATS-trilemma if lambda>0
periodogram	Periodogram (as generated by function per)
Lag	Backcast (Lag>0), Nowcast (Lag=0) or Forecast (Lag<0)
Gamma	Generic target specification: typically symmetric Lowpass (trend) or Bandpass (cycle) filters. Highpass and anticipative allpass (forecast) can be specified too.
eta	Customization parameter: Smoothness is emphasized in the ATS-trilemma if eta>0
cutoff	Specifies start-frequency in stopband from which Smoothness is emphasized (corresponds typically to the cutoff of the lowpass target). Is not used if eta=0.
i1	Boolean. If T a first-order filter constraint in frequency zero is obtained: amplitude of real-time filter must match weight_constraint (handles integration order one)
i2	Boolean. If T a second-order filter constraint in frequency zero is obtained: time-shift of real-time filter must match target (together with i1 handles integration order two)

Value

b optimal filter coefficients: MSE-design if lambda=eta=0

trffkt Complex transfer function of optimal (univariate) filter

wiaidp/MDFA documentation built on June 26, 2019, 1:07 p.m.