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Covariance matrix of coefficients with GARCH models
Posted: Sat Jul 04, 2015 7:07 am
by strypste
HI there
I estimated a GARCH model and now I want to calculate the variance covariance matrix of the coefficients (I can obtain this by View -> Covariance matrix). HoBut how is this calculated in Eviews?
As there is a constant in the mean and variance equation, X'X is not invertible, where X is a matrix of all the regressors in the model. So how can we possible do this?
Best
S
Re: Covariance matrix of coefficients with GARCH models
Posted: Sat Jul 04, 2015 9:15 am
by startz
A thoughtful question, but why do you think X'X is not invertible? The variables in the mean and variance equations don't enter linearly.
Re: Covariance matrix of coefficients with GARCH models
Posted: Mon Jul 06, 2015 2:58 am
by strypste
Hi Startz
Thanks for your answer. So how can I calculate it then? I looked online and textbooks, but was unsuccessful in finding an answer. Any references?
Best
s
Re: Covariance matrix of coefficients with GARCH models
Posted: Mon Jul 06, 2015 6:26 am
by startz
I believe the GARCH variance is just the usual maximum likelihood variance estimator, but maybe one of the EViews folks or Trubador could chime in.
Re: Covariance matrix of coefficients with GARCH models
Posted: Mon Jul 06, 2015 1:19 pm
by trubador
Startz is right. You can try the following and see it for yourself:
Code: Select all
'Create a workfile
wfcreate u 1000
'Generate an exogenous series
series x = nrnd
'Generate a y series that follows a GARCH-X(1,1) dynamic (use TSDGP add-in)
tsdgp(meanconst="5",meanexog="2*x",varconst="1", arch = "0.08", garch="0.91") y
'Estimate the model
equation eq.arch(backcast=1) y c x
'Save the gradients and store as a matrix
eq.makegrads(n=grads)
stom(grads,gradmat)
'Compute the covariance matrix (OPG)
sym varcov=@inverse(@transpose(gradmat)*gradmat)
'Save the covariance matrix from the estimated equation for comparison
sym eqcov = eq.@coefcov
Re: Covariance matrix of coefficients with GARCH models
Posted: Mon Jul 06, 2015 2:43 pm
by strypste
Brilliant! Thanks you so much for this. Very much appreciated!
Best
s