How to estimate a SDM with IV method?

Hi,

I have a cross section of 158 spatial units. Robust LM tests suggest me to
estimate a Spatial Durbin Model (SDM). However, given the non-normality of
OLS residuals, the presence of
heteroschedasticity and the presence of one endogenous explanatory
variable, I should not use the classic "lagsarlm" function, since I should
not use ML methods.
The point is that I did not find any package in R that estimate a SDM with
IV method (hence correcting for endogeneity and heteroschedasticity).

Would you suggest any strategy?

You are quite correct that the structuring of the stsls (and equivalent 
heteroskedastic version in the sphet package) makes it effectively 
impossible to fit a Spatial Durbin model. Even if one tries (using higher 
lags by hand), the results are typically numerically unstable. So you are 
left with ML - I would not worry about the distribution of the residual 
too much, but analysis of outliers from the influence measures of the 
linear fit might suggest a missing variable, or possibly a dummy that 
could releive som heteroskedasticity. You could check this out on the 
linear model first, and then use ML to fit the improved SDM model.

The only SDM with heteroskedastic errors that I know of is the Bayesian 
approach in the Matlab toolbox, documented in LeSage and Pace (2009). As 
I'm sure you know, you need to take the impacts of the RHS variables into 
account, rather than interpreting the SDM coefficients - this is provided 
for in impacts() methods in spdep for the SDM model.

Hope this helps,

Roger

Paolo

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Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no