Hi everyone,
I am new to spatial econometrics, the following questions may seem "stupid",
but they have been bothering me for a long period. I hope I can get some
good inputs here. Appreciate any help.
I used Spdep to estimate a set of spatial hedonic housing price models,
e.g., spatial lag and error models. The estimation is based on a modeling
sample, which is random sampled as 90% of the whole observations. Now I want
to finish the prediction for the testing sample (the rest 10%). The trouble
for me currently is how to create the weight matrix for the testing sample.
In the modeling process, the weight matrix is created based on the
observations in the modeling sample. Now for the testing sample, the weight
for each testing observation is supposed to be also only from the neighbors
in the modeling sample. I consulted with some guys, they said I have to
write codes in order to calculate it. I am wondering whether R has the
functions to calculate the distance between two sets of spatial points,
i.e., for each observation in the testing sample, to get the K nearest
"observations in the modeling sample". If this is achievable, then the
distance-based weight matrix and KNN weight matrix will be able to get.
Then another issue is about contiguity-based weight matrix. I personally
think this type of weight matrix is not suitable for prediction. I created
the rook/queen contiguity weight matrix based on the delaunay triangles
converted from point observations. For prediction, for each observation in
the testing sample, it is not easy to calculate the contiguity weight, which
is supposed to be from the border-shared observations in the modeling
sample. I was told that I can construct the contiguity weight matrix for the
whole set of observations, including both the modeling and testing sample,
however, in this case, the border-shared regions for one testing observation
may be also from the testing sample. I think this is not the correct way.
I hope the above text is understandable. also if sombody knows there may
exist some packages which can directly support the estimation and
prediction, that will be great.
thanks,
Dong
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