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

100 PONTIFICIAE ACADEMIAE SCIENTIARVM SCRIPTA VARIA - 28 not be remarkably important as the small sample variances of such estimators are infinite in some cases. Ordinary least squares certainly does have the property of finite small sample variances under ordinary conditions, however defective it may be for other reasons. Ordinary least squares estimates of the reduced form equations may therefore be appropriate ones to consider if one is willing to assume that serial correlation is animportant. Note that this is not quite the same as the situation as regards structural estimation already discussed. In that con- text several strong assumptions have to be nearly satisfied in order to justify the use of ordinary least squares. In the present context, only the assumption of no serial correlation must be approximately satisfied; if it is, the remaining argument against ordinary least squares is the one of lack of asymptotic effi- ciency and this may be by no means decisive in a world of relatively small samples (18). In practice, however, ordinary least squares estimation of the reduced form of a large economy-wide model is simply incapable of accomplishment. If all lagged endogenous vari- ables are treated as predetermined, the number of exogenous and predetermined variables in any but the most aggregative economy-wide model is simply too large to permit this type of estimation in the presence of the relatively low number of obser- vations ordinarily available. 2. FULL-INFORMATION ESTIMATORS We now discuss the class of full-information estimators out of what is perhaps the natural order. because it is relatively (5) All of our discussion of the effects of serial correlation has over- looked the existence of estimation techniques designed precisely to deal with that problem. See for example JoHNsTON [15, pp. 192-195] and THEIL (32, pp. 219-225]. All of these techniques, however, assume that there are no lagged endogenous variables in the model, and we have prin- cipally been concerned with the problems raised bv serial correlation when there are such lagged variables 61 Fisher - pag. 16

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