sigex.extract: Computes signal extraction estimates with two standard errors

In jlivsey/sigex: Ecce Signum: Multivariate Signal Extraction

Computes signal extraction estimates with two standard errors

Description

Background: A sigex model consists of process x = sum y, for stochastic components y. Each component process y_t is either stationary or is reduced to stationarity by application of a differencing polynomial delta(B), i.e. w_t = delta(B) y_t is stationary. We have a model for each w_t process, and can compute its autocovariance function (acf), and denote its autocovariance generating function (acgf) via gamma_w (B). The signal extraction filter for y_t is determined from this acgf and delta. param is the name for the model parameters entered into a list object with a more intuitive structure, whereas psi refers to a vector of real numbers containing all hyper-parameters (i.e., reals mapped bijectively to the parameter manifold)

Usage

sigex.extract(data.ts, my.filter, mdl, param)

Arguments

data.ts	A T x N matrix ts object
my.filter	List object corresponding to the output of sigex.signal, a list object of f.mat and v.mat. f.mat: array of dimension c(T,N,T,N), where f.mat[,j,,k] is the signal extraction matrix that utilizes input series k to generate the signal estimate for series j. v.mat: array of dimension c(T,N,T,N), where v.mat[,j,,k] is the error covariance matrix arising from input series k used to generate the signal estimate for series j.
mdl	The specified sigex model, a list object
param	The model parameters entered into a list object

Value

list object with extract, upp, and low extract: T x N matrix of the signal estimates upp: as extract, plus twice the standard error low: as extract, minus twice the standard error

jlivsey/sigex documentation built on May 25, 2024, 4:17 a.m.