Many thanks.

On Wed, Mar 27, 2013 at 3:50 AM, Achim Zeileis-4 [via R] <
ml-node+s789695n4662592h54@n4.nabble.com> wrote:

> On Tue, 26 Mar 2013, SHISHIR MATHUR wrote:
>
> > Thanks for the reply Achim. The reason I suspect autocorrelation is
> > because I think that  within the same neighborhood, homes sold a few
> > months back are likely to impact the price of homes sold subsequently.
>
> This may well be spatial (auto)correlation rather than temporal
> autocorrelation.
>
> > In fact the DW test and Breusch-Pagan test come out to be significant.
> > So even though the data is not time series (that is, I do not have
> > repeated observations for the same house),   however, the houses sold
> > close in time to each other are in the data set.
>
> If there is a unique ordering of all observations by time, then you could
> in principle apply an autocorrelation correction for the data, e.g., via
> Newey-West.
>
> But from what you describe above, it seems to be more important to capture
> spatial effects in the data, e.g., by using a spatial lag model (see
> lagsarlm in "spdep") or by using an additive spatial effect (see e.g. gam
> in "mgcv").
>
> > Thanks,
> > Shish
> >
> > On Tue, Mar 26, 2013 at 3:51 PM, Achim Zeileis <[hidden email]<http://user/SendEmail.jtp?type=node&node=4662592&i=0>>
>
> > wrote:
> >       On Tue, 26 Mar 2013, SHISHIR MATHUR wrote:
> >
> >             Hello:
> >             My dataset set contains several thousand rows of
> >             data, with each row
> >             containing information for a house. The variables
> >             include the sale price of
> >             the house, the quarter and year of sale, the
> >             attributes of the house, and
> >             the attributes of the neighborhood and the city in
> >             which the house is
> >             located. The data is for a 10-year period. No house
> >             is repeated in the
> >             dataset. In summary, the dataset can be termed
> >             pooled cross-section data.
> >
> >             My question: Can I estimate Newey-West HAC standard
> >             errors for a model that
> >             estimates the effect of various independent
> >             variables on the sale price of
> >             the house?  My understanding is that Newey-West can
> >             be used for time series
> >             and panel data. However, I am not sure whether it
> >             can be used for pooled
> >             cross-section data.  If yes, can you refer me to a
> >             specific source, such as
> >             a paper or a book?
> >
> >
> >       The result of your aggregation is a cross-section data set.
> >       Thus, there should be no correlation between the different
> >       observations - or in other terms, the ordering of your
> >       observations is completely arbitrary.
> >
> >       Consequently, there may be heteroskedasticity but not
> >       autocorrelation. So you may use HC standard errors but HAC
> >       should not be necessary. (Using HAC standard errors will still
> >       be consistent but less efficient.)
> >
> >
> >             --
> >             Best,
> >             Shish
> >
> >                     [[alternative HTML version deleted]]
> >
> >             ______________________________________________
> >             [hidden email]<http://user/SendEmail.jtp?type=node&node=4662592&i=1>mailing list
> >             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.
> >
> >
> >
> >
> > --
> > Best,
> > Shishir
> >
> >
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-- 
Best,
Shishir




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