MDFA_MSE: Simplified call for multivariate MSE estimation: no...
In wiaidp/MDFA: Signalextraction and Forecasting Based on the Multivariate Direct Filter Approach (MDFA)
Description
Usage
Arguments
Value
Description
Simplified call for multivariate MSE estimation: no constraints, no regularization, no customization
Usage
1
Arguments
L
Filter-length
weight_func
Spectrum: DFT-matrix or alternative (for example model-based) estimate: first column is the target variable, additional columns are explanatory variables
Lag
Nowcast (Lag=0), Forecast (Lag<0), Backcast (Lag>0)
Gamma
Generic target specification: typically symmetric Lowpass (trend) or Bandpass (cycle) filters. Highpass and anticipative allpass (forecast) can be specified too
Value
mdfa_obj MDFA object
wiaidp/MDFA documentation built on June 26, 2019, 1:07 p.m.
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Description Usage Arguments Value
Description
Simplified call for multivariate MSE estimation: no constraints, no regularization, no customization
Usage
1 |
Arguments
L |
Filter-length |
weight_func |
Spectrum: DFT-matrix or alternative (for example model-based) estimate: first column is the target variable, additional columns are explanatory variables |
Lag |
Nowcast (Lag=0), Forecast (Lag<0), Backcast (Lag>0) |
Gamma |
Generic target specification: typically symmetric Lowpass (trend) or Bandpass (cycle) filters. Highpass and anticipative allpass (forecast) can be specified too |
Value
mdfa_obj MDFA object
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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