Vmiss: VARMA Model with Missing Value

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

VARMA Model with Missing Value

Description

Assuming that the model is known, this program estimates the value of a missing data point. The whole data point is missing.

Usage

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

Arguments

zt	A T-by-k data matrix of a k-dimensional time series
piwgt	The pi-weights of a VARMA model defined as piwgt=[pi0, pi1, pi2, ....]
sigma	Positive definite covariance matrix of the innovations
tmiss	Time index of the missing data point
cnst	Constant term of the model
output	A logical switch to control output

Details

Use the least squares method to estimate a missing data point. The missing is random.

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

Vpmiss

Examples

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

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