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. Thanks in advance. Amitha Puranik.
Imputation of missing spatial areal data
3 messages · Roger Bivand, Amitha Puranik
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 at nhh.no https://orcid.org/0000-0003-2392-6140 https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
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 at 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 at nhh.no https://orcid.org/0000-0003-2392-6140 https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en