Vector autoregression with Newey-West standard errors

Hi

I wasn't aware of the fact that HAC is not designed for time series model 
(thanks Achim!). But nevertheless I think that HC is still usable, well at 
least I saw it in couple of papers dealing with times series.

So if you still want to use an HC, two solutions:

A solve the problem (workaround):

The problem is actually in an inconsistency in VAR() when working with 
type=const or both, as in your case.

See from your example:
model.matrix(a)
model.matrix(temp.lm)

to get it working you should replace:
       attr(equation[[colnames(yend)[i]]]$terms, "intercept") <- 1
by:
       attr(equation[[colnames(yend)[i]]]$terms, "intercept") <- NULL

I'll see with Bernhard how to update it.

B: I was working myself on a Eicker -White cov-mat and implemented it in a 
package (dev version of tsDyn) that also contains VAR, so you could use this 
one.

Bests


Achim Zeileis a ?crit :

On Tue, 9 Jun 2009, Liviu Andronic wrote:

Dear all,

I'm currently fitting vector autoregression using VAR() from package
`vars'. It estimates VAR by using OLS, and by default it provides
"naive" standard errors (not HC and not HAC).

The standard HAC approach only works if you have no lagged dependent 
variables among the regressors. The idea is that either (1) you don't model 
the autoregressive structure at all and just capture it in the correction 
of the standard errors or (2) you model the autoregressive structure by the 
model and need no correction of the standard errors.

However, I would like to obtain Heteroskedasticity-Autocorrelation
Consistent standard errors using NeweyWest() from package `sandwich',
which handles principally `lm' and `glm' objects.

The approach in "sandwich" is fully object-oriented, see
  vignette("sandwich-OOP", package = "sandwich")

I noticed that the
VAR() returns an object of class `varest', which contains a list of
fitted `lm' objects. So I tried to apply NeweyWest() to individual
`lm' components of `varest', unsuccessfully.

class(temp)

[1] "varest"

class(temp$varresult$e)

[1] "lm"

temp.lm <- temp$varresult$e
class(temp.lm)

[1] "lm"

require(sandwich)
NeweyWest(temp.lm)

Error in AA %*% t(X) : requires numeric matrix/vector arguments
In addition: Warning message:
In ar.ols(x, aic = aic, order.max = order.max, na.action = na.action,  :
 model order: 1singularities in the computation of the projection
matrixresults are only valid up to model order0

vcovHAC(temp.lm)

Error in bread. %*% meat. : non-conformable arguments


I would have expected the above to have worked. For "standard" `lm'
objects, I never had any such issues:

That's because the internal structure of "varest" objects does not contain 
`standard' "lm" objects... The constant/intercept is special cased and 
hence
  bread(temp$varresult$e)
  meatHAC(temp$varresult$e)
are non-conformable. The warning from ar.ols() is thrown because 
prewhitening is used by default which really doesn't make any sense for 
VARs.

To adapt "sandwich" to a new model class, bread() and estfun() methods need 
to be supplied (see the vignette above). This wouldn't be very difficult 
for "varest" objects, but as pointed out above, I don't think it is very 
useful.

Best,
Z