SEMAINE D'ETUDE SUR LE ROLE DE L ANALYSE ECONOMETRIQUE ETC. 
40. 
easy to dispose of it. We shall then be free to turn our atten- 
tion to the class of estimators ordinarily used in these problems 
the limited-information class. 
It is customary in these discussions to pay lip-service to 
full-information maximum likelihood as the optimal estimator 
using all information available and then to dismiss it in prac- 
tice as too difficult of computation. While it is still true that 
such computational difficulties are still prohibitive in practice 
for even moderately large systems (1%), such dismissal no lon- 
ger suffices. This is the case because there are now two full 
information estimators which are known to have the same 
asymptotic distribution as full-information maximum likeli- 
hood and which are not particularly difficult to compute. These 
are the three-stage least squares estimator proposed by ZELLNER 
and THEIL and the linearized maximum likelihood method of 
ROTHENBERG and LEENDERS (!°). Since the known virtues of 
full-information maximum likelihood are all asymptotic, com: 
putational difficulty can no longer be considered a valid reasor 
for not using some such method. 
As it happens, however, there are more cogent reasons thar 
computational difficulty for the abandonment of full-informa- 
tion methods in practice. However desirable the properties of 
full-information methods may be in principle when all assump- 
tions are met, such estimators suffer relatively heavily from 
a lack of robustness in the presence of common practical dif- 
ficulties. Thus KLEIN and NAKAMURA have suggested that 
full-information maximum likelihood is more sensitive to 
multicollinearity than are limited-information estimators (%) 
(18) The difficulties are being overcome, however. See EISENPRESS [7%] 
(19) ZELLNER and THEIL [37]; ROTHENBERG and LEENDERS [26]. Ro- 
THENBERG and LEENDERs give the proof that these estimators have the same 
asymptotic distribution as full-information maximum likelihood. See also 
SARGAN [27] and MapaNsky [20]. BrowN’s simultaneous least squares {6?] 
[which is a member of the full-information class] is known to be consistent 
but is not known to have the same asvmptotic distribution as the other 
members. 
(2°) KLEIN and NAKAMUR/ 
‘vi Fisher - pag. 
1”,