Dear Prof. Bivand and listers: 

I started with spatial econometrics and R a few months ago and this is the first time I write here. My apologies if I miss something. 

In am a bit confused about the final weight matrix I am using with spdep. Initially I start with an inverse distance matrix to the 5-nearest neighbors, so asymmetric general weights. I decided this matrix as my baseline because of several reasons: I research about distance, Griffith (1996) rules of thumb, inspection of the plots of links... 

I build the listw style W (row-normalization) to apply errorsarlm or lagsarlm. But at least for method eigen, theses functions use the function listw2U which changes my listw. 

I will try to write separate questions or sentences about my doubts, though they are related: 

Q1 - I understand that the similar.listw function (http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=spdep:similar.listw) is the same than listw2U but I am not sure. 

Q2 - I see that listw2U uses the function make.sym.nb, adding the neighbors that were asymmetric. If this is true, I miss the property of asymmetry, so I do not see the point of starting with k-nearest neighbors. 

Q3 - Similarly, I understand that these functions are applying Ord (1975) transformation, though the notation here confuses me: 
http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=spdep:lm.morantest 
The helper function listw2U() constructs a weights list object corresponding to the sparse matrix 1/2 (W + W'). 

Q4 - The help in moran.test explains that for inherently non-symmetric matrices, such as k-nearest neighbour matrices, listw2U() can be used to make the matrix symmetric. Does this means that I should pass my W matrix through listw2U before Moran test?. In row-normalizatization W style or in B style with inverse distance weights? 

Q5 - In Elhorst (2010) (Fisher & Getis book, page 380) I see that Ord transformation keeps unchanged the mutual proportions between the elemets of W, which is relevant for inverse distances (Anselin 1998, 23-24). I understand that if I want to keep the interpretation of inverse distances I should apply this transformation to the listw style B, without row-normalization, with my general spatial weights of inverse distances. But the method eigen in, for instance, lagsarlm or ASDAR page 284 says that Ord normalization can be used just with W matrixes. I have tried this method to estimate spatial models with my listw in both B and W styles, but I am not sure what I am doing at the end in each case. 

Q7 - Similarly to standardization, the fact of making symmetric the matrix, loses the geographical interpretation. The unit A can be the nearest neighbors of B but not the contrary so their main links would not be reciprocal. I am not sure about in which ways the issue of symmetry in the nb list is a different issue from normalization of weights. Both things usually are discussed at the same time, but they are conceptually different 

Q8 - If it is a problem of estimation, I can use other methods to keep my matrix asymmetric and/or with no-standardized weights. Which one would be better?. 

Q9 - Any idea about how the splm packages deals with some of the previous issues? 

Q10 - This question is more general. I am still not sure if I understand well the reasons for normalization. Maybe it is to ensure the estimation by keeping the eigen values in the right range. Right now I do not care about the easier interpretation of the parameters after row-normalization. My question is if normalization helps to correct spatial autocorrelation. If so, why does it helps if it, at least with row-normalization, loses the main information about the absolute distance to each neighbor?. Why the impact of each unit by all other unit should be equalized?. Is each unit equally open, equally close to the other ones?. Or this is just to avoid imposing structure on the spatial dependence so general spatial weights are not that useful (just relative distances)? 

Any thinking about these doubts would be very appreciated. Thank you very much 

Fernando 


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