mix_se-methods: Compute standard errors of estimates of MixAR models

In GeoBosh/mixAR: Mixture Autoregressive Models

Compute standard errors of estimates of MixAR models

Description

Compute standard errors of estimates of MixAR models.

Usage

mix_se(x, model, fix_shift)

Arguments

x	time series.
model	MixAR model, an object inheriting from class “MixAR”.
fix_shift	logical. Should the shift paramters be fixed? Default is FALSE.

Details

For formulas used in the computation, see \insertCiteWongPhD;textualmixAR.

Value

a list with components:

standard_errors	Standard error of parameter estimates,
covariance_matrix	The covariance matrix, obtained as inverse of the information matrix,
Complete_Information	Complete information matrix,
Missing_Information	Missing information matrix.

Methods

signature(x = "ANY", model = "list")

signature(x = "ANY", model = "MixAR")

signature(x = "ANY", model = "MixARGaussian")

Author(s)

Davide Ravagli

References

\insertRef

WongPhDmixAR

Examples

## Example with IBM data

## data(ibmclose, package = "fma")

moWLprob <- exampleModels$WL_ibm@prob    # 2019-12-15; was: c(0.5339,0.4176,0.0385)     
moWLsigma <- exampleModels$WL_ibm@scale  #                  c(4.8227,6.0082,18.1716)
moWLar <- list(-0.3208, 0.6711,0)        # @Davide - is this from some model?

moWLibm <- new("MixARGaussian", prob = moWLprob, scale = moWLsigma, arcoef = moWLar)

IBM <- diff(fma::ibmclose)
mix_se(as.numeric(IBM), moWLibm, fix_shift = TRUE)$'standard_errors'

GeoBosh/mixAR documentation built on Oct. 28, 2024, 7:01 p.m.