MTS: Vpmiss – R documentation

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Vpmiss

Partial Missing Value of a VARMA Series

Description

Assuming that the data is only partially missing, this program estimates those missing values. The model is assumed to be known.

Usage

Vpmiss(zt, piwgt, sigma, tmiss, mdx, cnst = NULL, output = T)

Arguments

`zt`	A T-by-k data matrix of a k-dimensional time series
`piwgt`	pi-weights of the model in the form piwgt[pi0, pi1, pi2, ....]
`sigma`	Residual covariance matrix
`tmiss`	Time index of the partially missing data point
`mdx`	A k-dimensional indicator with "0" denoting missing component and ""1" denoting observed value.
`cnst`	Constant term of the model
`output`	values of the partially missing data

Value

Estimates of the missing values

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 6). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

Examples

#data("mts-examples",package="MTS")
#gdp=log(qgdp[,3:5])
#m1=VAR(gdp,1)
#piwgt=m1$Phi; cnst=m1$Ph0; Sig=m1$Sigma
#mdx=c(0,1,1)
#m2=Vpmiss(gdp,piwgt,Sig,50,mdx,cnst)

MTS

All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

v1.0

Artistic License 2.0

Authors

Ruey S. Tsay and David Wood

Initial release

2018-10-8