Vpmiss: Partial Missing Value of a VARMA Series

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

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.

See Also

Vmiss

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 documentation built on April 11, 2022, 5:07 p.m.