mat_func: This function sets-up the design matrix of the generic...

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

Description

This function sets-up the design matrix of the generic optimization problem: it combines data, regularization features and filter constraints

Usage

mat_func(i1, i2, L, weight_h_exp, lambda_decay, lambda_cross, lambda_smooth,
  Lag, weight_constraint, shift_constraint, grand_mean, b0_H0, c_eta, lag_mat)

Arguments

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)
L	Filter-length
weight_h_exp	DFT of explanatory variables
lambda_decay	Regularization: decay term
lambda_cross	Regularization: cross-sectional term
lambda_smooth	Regularization: smoothness term
Lag	Nowcast (Lag=0), Forecast (Lag<0), Backcast (Lag>0)
weight_constraint	Constraint vector in the case i1==T
shift_constraint	Constraint vector in the case i2==T
grand_mean	Boolean: if T then a grand-mean parametrization is imposed (default is F)
b0_H0	Regularization: shrinkage target (arbitrary designs can be replicated by imposing strong regularization)
c_eta	Boolean: impose mild/strong smoothness customization (default is F)
lag_mat	Matrix for implementing effective lags in a mixed-frequency setting

Value

des_mat Design matrix

reg_mat Bilinear form (regularization matrix)

reg_xtxy Constants from regularization

w_eight Constants from filter constraints

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