problems of a particular equation in the model. Examples are given. Further generalizations are also considered but only conditional results are given. Further work is suggested.

[This paper looks at some aspects of the three stage least squares approach to estimating simultaneous econometric models. Three stage least squares is derived along the lines of best linear unbiased estimators in classical regression, whereby it is indicated that the usual assumption of non-singularity of the disturbance covariance matrix is unnecessary. Consistency of the estimator is shown as is the irrelevance of exactly identified equations to the estimation of other equations in a model when three stage least squares is used. Also included are an easily computed test for the validity of all specified overidentifying restrictions, and a method of efficiently estimating the reduced form using only the information contained in structural equations that are thought to be well specified.]

R. L. Basmann, D. H. Richardson, R. J. Rohr, An Experimental Study of Structural Estimators and Test Statistics Associated with Dynamical Econometric Models, Econometrica, Vol. 42, No. 4 (Jul., 1974), pp. 717-730

R. L. Basmann, D. H. Richardson, R. J. Rohr, Finite Sample Distributions Associated with Stochastic Difference Equations--Some Experimental Evidence, Econometrica, Vol. 42, No. 5 (Sep., 1974), pp. 825-839

In applications of linear regression analysis, the unknown error covariance matrix has to be somehow estimated. This can lead to biased estimates of the covariance matrix of the regression coefficients. Since such bias is difficult to eliminate completely, its sensitivity to alternative estimates of error covariances is studied by Watson, Theil, Malinvaud, and others with the help of bounds on the bias derived under certain assumptions. This paper gives similar bounds under less restrictive assumptions, and illustrates them in the context of heteroscedasticity and autocorrelation problems. In particular, for the first order error autocorrelation coefficient of p the upper bound on proportionate bias is shown to be reasonably approximated by (1 + p)/(l - p) - 1.

This paper is concerned with the problem of estimating demand and supply schedules in disequilibrium markets. The results of Fair and Jaffee are expanded in three ways. (1) Their directional method I is modified to yield consistent estimates. (2) A maximum likelihood alternative to their quantitative method is proposed. (3) The price equation is generalized to be a multivariate, stochastic function, and a method is proposed for estimating demand and supply schedules in this case.