Greetings once again,
 
I've been searching to explain some odd results that I'm having with
general moments (GM) estimators with the spatial error model.  The
spatial autoregressive parameter lambda is outside the parameter space
and is usually 1.1 to 1.2.
 

This has occurred in several software packages and routines, including
those in spdep.  The sample sizes of my datasets are 3,000 or greater
and include site-specific information from agricultural experiments,
i.e. yield monitor data.  All variables are highly spatially
autocorrelated.  
 
In Kelejian and Prucha's (2006) paper "Specification and Estimation of
Spatial Autoregressive Models with Autoregressive and Heteroskedastic
Disturbances,"
<http://www.econ.umd.edu/~prucha/Papers/WP_Prucha_3_2006.pdf>  they
discuss in section 2.2 that the lambda needs to be transformed with the
scalar normalization factor used to standardize the weights matrix.
This of course, assumes I understood the K&P paper.  If the weights
matrix is globally standardized, this normalization factor is simple to
obtain, but with row-standardization it is not so clear.  In addition,
the row-standardized weights matrix produces results that I might expect
while the globally standardized weights matrix produces more erratic
coefficients.  
 
Any suggestions or other comments? 
 
Thank you,
 
Terry
 

 

 

Terry W. Griffin

Graduate Research Assistant

Agricultural Economics

Purdue University

403 W State St

West Lafayette, IN 47907

765-494-4257

http://web.ics.purdue.edu/~twgriffi/
<http://web.ics.purdue.edu/~twgriffi/> 

 

 


	[[alternative HTML version deleted]]


