statehistory.pci: Generates the sequence of inferred states of a partial...
In partialCI: Partial Cointegration

Description Usage Arguments Details Value Author(s) See Also Examples

Generates the sequence of inferred states of a partial cointegration model

1	statehistory.pci(A, data = A$data, basis = A$basis)

`A`	An object returned by `fit.pci` representing a partial cointegration fit.
`data`	The data history for which the inferred states are to be computed. This should be a `(k+1) x n` matrix, where `k` is the number of independent variables and `n` is the number of observations. If this is omitted, then uses the data history that was used in fitting the model `A`.
`basis`	The coefficients of the independent variables. This is a vector of length `k`. If this is omitted, then uses the coefficients that were computed in fitting the model `A`.

Computes the expected internal states of the model over the course of the data history.

Returns a data.frame with the following columns:

`Y`	The variable being modeled
`X1,...,X_N`	The independent variables
`Z`	The residual series `Y - beta %*% X`
`M`	The estimated state of the mean reverting component
`R`	The estimated state of the random walk component
`eps_M`	The innovation to the mean reverting component
`eps_R`	The innovation to the random walk component

Matthew Clegg matthewcleggphd@gmail.com

Christopher Krauss christopher.krauss@fau.de

Jonas Rende jonas.rende@fau.de

egcm Engle-Granger cointegration model

partialAR Partially autoregressive models

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

YX <- rpci(n=1000, beta=c(2,3), sigma_C=c(0.1,0.1), rho=0.9, sigma_M=1, sigma_R=2)
f <- fit.pci(YX[,1], YX[,2:ncol(YX)])
statehistory.pci(f)