Thank you. The issue is resolved by scaling the data in millions.
Saba
On Saturday, 19 December 2015, 15:06, Achim Zeileis
<achim.zeil...@uibk.ac.at> wrote:
On Sat, 19 Dec 2015, Saba Sehrish via R-help wrote:
> Hi I am using NeweyWest standard errors to correct lm( ) output. For example:
> lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5)
> vcovNW<-NeweyWest(lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5))
>
> I am using package(sandwich) for NeweyWest. Now when I run this command, it
> gives following error:
> Error in solve.default(diag(ncol(umat)) - apply(var.fit$ar, 2:3, sum))
> :system is computationally singular: reciprocal condition number = 7.49468e-18
>
> Attached herewith is data for A&B, A1,A2,A3,A4,A5,B1,B2,B3,B4,B5 are
> simply lag variables. Can you help me removing this error please?
Without trying to replicate the error, there are at least two issues:
(1) You should scale your data to use more reasonable orders of magnitude,
e.g., in millions. This will help avoiding numerical problems.
(2) More importantly, you should not employ HAC/Newey-West standard errors
in autoregressive models. If you use an autoregressive specification, you
should capture all relevant autocorrelations - and then no HAC estimator
is necessary. Alternatively, one may treat autocorrelation as a nuisance
parameter and not model it - but instead capture it in HAC standard
errors. Naturally, the former strategy will typically perform better if
the autocorrelations are more substantial.
> Saba
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
