This Lecture Note deals with asymptotic properties, i.e. weak and
strong consistency and asymptotic normality, of parameter
estimators of nonlinear regression models and nonlinear structural
equations under various assumptions on the distribution of the
data. The estimation methods involved are nonlinear least squares
estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non
linear weighted robust M-estimation (NLWRME) for the regression
case and nonlinear two-stage least squares estimation (NL2SLSE) and
a new method called minimum information estimation (MIE) for the
case of structural equations. The asymptotic properties of the
NLLSE and the two robust M-estimation methods are derived from
further elaborations of results of Jennrich. Special attention is
payed to the comparison of the asymptotic efficiency of NLLSE and
NLRME. It is shown that if the tails of the error distribution are
fatter than those of the normal distribution NLRME is more
efficient than NLLSE. The NLWRME method is appropriate if the
distributions of both the errors and the regressors have fat tails.
This study also improves and extends the NL2SLSE theory of Amemiya.
The method involved is a variant of the instrumental variables
method, requiring at least as many instrumental variables as
parameters to be estimated. The new MIE method requires less
instrumental variables. Asymptotic normality can be derived by
employing only one instrumental variable and consistency can even
be proved with out using any instrumental variables at all."
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