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'ÉTUDE SUR LE ROLE DE L’ANALYSE ECONOMETRIQUE ETC. 397 rem of Worp (!?). That theorem states that the inconsistency of least squares will be small the smaller are the correlations between the explanatory variables in the equation to be estim- ated and the disturbance from that equation and also the smaller is the variance of that disturbance. A perhaps more illuminat- ing way of looking at the same thing is to consider the disturb- ance as made up of a linear combination of omitted variables. The inconsistencies in the parameter estimates can then be shown to be equal to the coefficients of the multiple regression of the disturbance term on the explanatory variables (!3). For our purposes, the Proximity Theorem shows that if (R.1), (R.2), and (R.3) or (R.3*) hold approximately, the inconsistency of ordinary least squares will be small. Indeed, that inconsistency will be small in a given equation if the ap- propriate columns of (2.7 premultiplied by the inverse of the variance-covariance mau of the variables appearing on the right of that equation is smal. Since that inverse enters the ordinary least squares parameter estimates in precisely the same way, we may say that (roughly) relative inconsistencies will be small provided that W(o) and W(x) are small. Thus, if all terms above the diagonal in A are nearly zero; if cross-equation covariance between contem- porary disturbances is small; and if there is little serial cor relation, ordinary least squares will not do too badly. Unfortunately, there is reason to believe that this will not generallv be the case. The arguments given above for the (1) Worp and JURÉEN [34, p. 189 and pp. 37-38]. The Proximity Theorem as stated by WoLD is one concerning bias; we discuss inconsistency since unbiasedness is not in any case a property of least squares in models with lagged endogenous variables. See Hurwicz [14] (13) See FISHER [8] and THEIL [31] Fisher - pag. 1:

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