Vpmiss {MTS} | R Documentation |
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)
[Package MTS version 1.2.1 Index]