TY - JOUR
AU - Amemiya, Takeshi
TI - The Maximum Likelihood Stage Least Squares Estimator in the Nonlinear Simultaneous Equations Model
JF - National Bureau of Economic Research Working Paper Series
VL - No. 90
PY - 1975
Y2 - June 1975
DO - 10.3386/w0090
UR - http://www.nber.org/papers/w0090
L1 - http://www.nber.org/papers/w0090.pdf
N1 - Author contact info:
Takeshi Amemiya
Stanford University
E-Mail: amemiya@stanford.edu
AB - The consistency and the asymptotic normality of the maximum likelihood estimator in the general nonlinear simultaneous equation model are proved. It is shown that the proof depends on the assumption of normality unlike in the linear simultaneous equation model. It is proved that the maximum likelihood estimator is asymptotically more efficient than the nonlinear three-stage least squares estimator if the specification is correct, However, the latter has the advantage of being consistent even when the normality assumption is removed. Hausrnan' s instrumental-variable-interpretation of the maximum likelihood estimator is extended to the general nonlinear simultaneous equation model.
ER -