[R-SIG-Finance] Vector autoregression with Newey-West standard errors

[Matthieu Stigler]

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 écrit :
> 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
>
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