statehistory.par: Estimates hidden states of a partially autoregressive model
In partialAR: Partial Autoregression

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

Estimates hidden states of a partially autoregressive model

1	statehistory.par(A, data = A$data)

`A`	A `par.fit` object returned from a previous call to `fit.par`
`data`	A sequence of observed states

Based on the parameters of the model fitted by the previous call to fit.par, produces a data.frame containing the inferred hidden states of the process.

A data.frame with one row for each observation in data. The columns in the data.frame are as follows:

`X`	Value of the observed state (`data`) at this time
`M`	Estimated value of the mean-reverting component at this time
`R`	Estimated value of the random walk component at this time
`eps_M`	Estimated innovation to the mean-reverting component
`eps_R`	Estimated innovation to the random walk component

Matthew Clegg matthewcleggphd@gmail.com

Clegg, Matthew. Modeling Time Series with Both Permanent and Transient Components using the Partially Autoregressive Model. Available at SSRN: http://ssrn.com/abstract=2556957

fit.par

# A simple example to compare the fitted values of the mean-reverting
# component with the actual data
set.seed(1)
xactual <- rpar(1000, 0.9, 2, 1, include.state=TRUE)
xfit <- fit.par(xactual$X)
xstates <- statehistory.par(xfit)
summary(lm(xstates$M ~ xactual$M))

## Not run: 
    require(ggplot)
    xdf <- rbind(data.frame(data="actual", x=1:nrow(xactual), value=xactual$M), 
      data.frame(data="fitted", x=1:nrow(xstates), value=xstates$M))
    ggplot(xdf, aes(x=x, y=value, colour=data)) + geom_line()

## End(Not run)