View source: R/sigex.meaninit.r
sigex.meaninit | R Documentation |
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.
sigex.meaninit(mdl, data.ts, d)
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. |
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.
mdl: the updated sigex model, a list object
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