[R-sig-Geo] Imputation of missing spatial areal data

[Amitha Puranik]

Dear Roger,

Thank you for the quick response. I shall refer the article that you
recommended.

Kind regards,
Amitha Puranik.


On Mon, Oct 28, 2019 at 3:39 PM Roger Bivand <Roger.Bivand using nhh.no> wrote:

> On Mon, 28 Oct 2019, Amitha Puranik wrote:
>
> > Hello everyone,
> >
> > I would like to know whether it is possible to use the spatial
> > autoregressive model to impute missing values in aggregate data? If the
> > OLS model is replaced with SAR model in regression imputation, would it
> > lead to better estimates for missing values in a spatial data? Any
> > opinion/ suggestion is appreciated.
>
> Please see the article referenced in the help page for
> spatialreg::predict.sarlm():
>
> Michel Goulard, Thibault Laurent & Christine Thomas-Agnan, 2017 About
> predictions in spatial autoregressive models: optimal and almost optimal
> strategies, Spatial Economic Analysis Volume 12, Issue 2-3, 304-325
>
> The differences in the spatial error model would be through any
> differences in covariate coefficient values, but if the differences are
> large, the Hausman test for misspecification would fail. Your post nudged
> me to raise an issue on spatialreg about SLX prediction, which very likely
> also makes sense, and to check predictions where Durbin=TRUE more
> generally.
>
> Roger
>
> >
> > Thanks in advance.
> >
> > Amitha Puranik.
> >
> >       [[alternative HTML version deleted]]
> >
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>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
> https://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
>

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