With regard to large datasets, I cannot comment on how to estimate spatial
autoregressions in SAS. I have estimated a lattice model in S-PLUS (SAR)
using 3,107 observations. This took around 20 minutes on a 200 Mhz Pentium
I have published some papers about how to estimate large lattice models - I
have dealt with as many as 200K+ observations using some of these
techniques. I currently have two software packages I have written which I
am distributing for free (with correspondingly little emphasis on GUI).
I have a Fortran 90 based package with PC executable code. This relies on
nearest neighbors for specifying the spatial relations. One can choose the
number of neighbors and the weight given to them. To conduct maximum
likelihood you need the log of the determinant of the variance-covariance
matrix or its inverse. This package uses an approximation (which yields
confidence intervals) for the log-determinant. We have handled matrices as
large as 1M by 1M using this technique (see Barry and Pace, Linear Algebra
and its Applications, forthcoming).
Given the log of the determinant, we estimate via ML the model:
Y=X*B1+S*X*B2+alpha*S*Y+e, where S is the n by n spatial weight matrix (we
take the log-determinant of (I-alpha*S)). This corresponds to having
separately spatially lagged independent and dependent variables. It takes
under 10 seconds on a Pentium 233 MMX to find the neighbors, estimate the
log-determinant, and compute the maximum likelihood estimates for the 3,107
observation dataset. It yields profile likelihoods for the overall model
and many submodels. There are separate profile likelihoods for the lower
and upper bounds of the log-determinant and hence the model accounts for
this source of uncertainty. Hence, one can conduct likelihood ratio tests
Compressed this package with an example and some documentation takes under
1MB compressed and hence I can email it to whoever wishes to use it.
I have a second more comprehensive (at least for lattice models) package
written in Matlab. This does SAR, CAR, the model above, has Delaunay and
nearest neighbor weight matrices, and simulation routines. This also has
several example datasets and so forth. This package takes around 30MB and
cannot be so easily sent. If anyone truly wants it right away, I can burn a
CDROM and send it. However, I am planning on duplicating this on CDROM and
sending out some copies at the end-of-the month for those without a
Anyone who is interested in receiving these please send a message to
and put "spatial package" as the first two words
of the subject. I will send you either or both of these toolboxes.
Real Estate Research Institute
E.J. Ourso College of Business
Louisiana State University
Baton Rouge, LA 70803
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