# Spatial Autocorrelation Estimation Method

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## Spatial Autocorrelation Estimation Method

 I have a large pooled cross-section data set. ​ I would like to estimate/regress using spatial autocorrelation methods. I am assuming for now that spatial dependence is present in both the dependent variable and the error term.​ ​ My data set is over a period of 4 years, monthly data (54 periods). For this means, I've created a time dummy variable for each time period.​ ​ I also created a weight matrix using the functions "poly2nb" and "nb2listw".​ ​ Now I am trying to figure out a way to estimate my model which contains a really big data set.​ ​ Basically, my model is as follows: y = γD + ρW1y + Xβ + λW2u + ε​ ​ My questions are:​ ​ 1) My spatial weight matrix for the whole data set will be probably a enormous matrix with submatrices for each time period itself. I don't think it would be possible to calculate this.​ What I would like to know is a way to estimate each time dummy/period separately (to compare different periods alone). How to do it?​ ​ 2) Which package to use: spdep or splm?​ ​ Thank you and best regards,​ Robert​         [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
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