Abstract :
This paper considers the extension of the classical minimum distance approach for
the pooling of estimates with various rates of convergence. Under a setting where
relatively high rates of convergence can be attained, the minimum distance estimators
are shown to be consistent and asymptotically normally distributed. The constrained
estimates can be efficient relative to the unconstrained ones. The minimized
distance function is shown to be asymptotically χ2-distributed, and can be used as a
goodness-of-fit test for the constraints. As the extension is motivated by some social
interactions models, which are of interest in their own right, we discuss this approach
for the estimation and testing of a social interactions model.
