[Openspace] The econometric problem with islands
MMONTGOMERY at popcouncil.org
Wed Jun 21 16:03:27 CDT 2006
I'm searching for a good discussion of what goes wrong, in strictly econometric terms, when "islands" are included in a spatial error regression model. If there is one island, for instance, then our weight matrix has a row with only zero entries. What happens to the spatial error model likelihood function in this case?
I can think of three implications. (1) An island data point doesn't help us to identify the value of the spatial autocorrelation parameter. However, the other data points will be informative about this parameter, so we should be fine so long as we have enough connected observations. (2) We can't row-standardize the weight matrix. But row-standardization isn't necessary in specifying a weight matrix, it is just a nice option to have. (3) Islands provide useful information on the beta parameters of the regression model, and dropping them from the dataset means losing information on this part of the model.
So, what exactly happens to the likelihood function that causes things to break down?
Any advice would be much appreciated. I'm a newcomer to this listserve but am finding it really useful.
Mark R. Montgomery
Professor of Economics
State University of New York, Stony Brook
Senior Associate, Policy Research Division
1 Dag Hammarskjold Plaza
New York, NY 10017
mmontgomery at popcouncil.org
(212) 339-0673 (phone)
(212) 755-6052 (fax)
More information about the Openspace