sigex.meaninit: Adds trend regressors to an existing model

In jlivsey/sigex: Ecce Signum: Multivariate Signal Extraction

Adds trend regressors to an existing model

Description

Background: x is a multivariate time series (N x T), and each individual series can have its distinct set of regressors. So for each 1 <= j <= N, x[j,] is length T and has r_j number of length T regressors. There is a default regressor of polynomial time: suppose the time series has d unit roots (d >= 0), and this applies to each individual series (differencing polynomials are the same for all individual series in sigex). Then the regressor t^d for 1 <= t <= T is the default "mean effect". (Coefficients of lower order time polynomial effects cannot be identified.) When d=0, this is just the mean of the process. (Although it need not be stationary when d=0, any other non-stationary latent components are assumed to have mean zero for identifiability.) One can always add higher order time polynomial regressors, if desired.

Usage

sigex.meaninit(mdl, data.ts, d)

Arguments

mdl	The specified sigex model, a list object. mdl[[1]] is mdlK, gives ranks of white noise covariance matrix mdl[[2]] is mdlType, a list giving t.s. model class, order, and bounds mdl[[3]] is mdlDiff, gives delta differencing polynomials mdl[[4]] is list of regressors by individual series
data.ts	A T x N matrix ts object
d	Order of time polynomial trend regressor desired, labeled as "Trend". However, if a trend component exists in the model, then this d is ignored, and the number of unit roots is instead used to determine d.

Details

Notes: always use this function when setting up the model. First make all calls to sigex.add, then call sigex.meaninit, and then add additional regressors (if needed) with sigex.reg.

Value

mdl: the updated sigex model, a list object

jlivsey/sigex documentation built on March 20, 2024, 3:17 a.m.