Partial Missing Value of a VARMA Series

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Description

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

Usage

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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 partilly 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

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#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)

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