[R-sig-Geo] Spatial Autocorrelation Estimation Method

[Robert R]

I have a large pooled cross-section data set.
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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.​
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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.​
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I also created a weight matrix using the functions "poly2nb" and "nb2listw".​
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Now I am trying to figure out a way to estimate my model which contains a really big data set.​
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Basically, my model is as follows: y = γD + ρW1y + Xβ + λW2u + ε​
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My questions are:​
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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?​
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2) Which package to use: spdep or splm?​
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Thank you and best regards,​
Robert​

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