List members:
A few months ago I posted an email to the group concerning trouble I was having with estimating regressions that test and correct for spatial lag and error. My problem was that these tests as designed for R assume that the neighbors list is row standardized, while I have a weights matrix that is set up as the inverse distance between neighbors. I was concerned with converting my weights matrix into a neighbors format because this could result in the loss of potentially important information. Some what I have done is tested three ways to identify the weights matrix: 1) inverse distance 2) row standardized and 3) binary neighbors. When using each of the derived neighbors lists form these matrices in the LM test I still get the error: "Spatial weights matrix not row standardized" except in the application using the binary neighbors. Roger Bivand responded with the following questions (my answers are in bold): 1) Are the results from a binary IDW and a row standardized IDW very different? Response: The results for the LMerr, LMlag, RLMerr, RLMlag, and SARMA are not identical but statistically similar between 1) and 2) - but different between these and 3) - but perhaps this could simply be due to the definition of a neighbor used? I would think this might be getting into a theoretical question rather than an empirical one, but the posted error is what made me wonder if perhaps the results are invalid? 2) Is your IDW matrix full or sparse? Response: The matrix is full - no missing observations. 3) Can Moran's I be applied instead (despite its covering lots of misspecification problems)? Yes, and the results are somewhat consistent in identifying spatial autocorrelation - but this does not differentiate between spatial lag and error. 4) Are the IDW weights symmetric (probably, but not always)? Response: Yes, the weights are symmetric. 5) I'm not sure why distances should be helpful if the data are observed on areal units, so that measuring distances is between arbitrarily chosen points in those units, a change of support problem. But error correlation specified by distance does may be rather close to geostatistics, doesn't it? Response: Distances are important and useful because they imply a range of influences. My database includes a stratified random sample of households in the Amazon. Therefore, the closest "neighbors" are those with the largest possible influence on household land use (and other) decisions. These same neighbors would have the largest value for the inverse distance - the greater the number the greater the influence. This weighted influence (or differing degrees of influence) is lost when using dummy variables or a binary definition of neighbors. I have also run the spatial error, lag and combined models to correct for autocorrelation. I have attached my results for anyone that's interested. Any insights about the questions above or the estimations are welcome. Thanks. -Jill *************************************************** Jill L. Caviglia-Harris, Ph.D. Associate Professor Economics and Finance Department Salisbury University Salisbury, MD 21801-6860 phone: (410) 548-5591 fax: (410) 546-6208 ********************************************************* -------------- next part -------------- A non-text attachment was scrubbed... Name: Estimation results - pasture.doc Type: application/msword Size: 112640 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-sig-geo/attachments/20040903/60e79190/attachment.doc> |
Free forum by Nabble | Edit this page |