Study week on the econometric approach to development planning

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Monograph

Identifikator:: 1824422792

URN:: urn:nbn:de:zbw-retromon-217476

Document type:: Monograph

Title:: Study week on the econometric approach to development planning

Place of publication:: Amsterdam [u.a.]

Publisher:: North-Holland Publ. Co. [u.a.]

Year of publication:: 1965

Scope:: XLVII, 1259 S.; graph. Darst.

Digitisation:: 2022

Collection:: Economics Books

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Document type:: Monograph

Structure type:: Chapter

Title:: Dynamic structure and estimation in economy-wide econometric models / FranklinM. Fisher, Massachuetts Institute of Technology - Cambridge, Mass. - U.S.A.

Collection:: Economics Books

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”,

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