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.
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Roger Bivand
Department of Economics, Norwegian School of Economics,
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