This paper reports on some mathematical and analytical properties of a static nonlinear model of a national economy or, more generally, of a multisectored economy. The model is a nonlinear version of the well-known linear input-output model of Leontief. Conditions are given for the nonlinear model to be workable in the sense that (i) there is a unique nonnegative vector of output production levels for each nonnegative final-demand vector, and (ii) the vector of output levels depends in a certain reasonable manner on the finaldemand vector. Attention is also focused on several other properties of the model of economic interest. For example, it is shown that propositions completely analogous to the LeChatelier-Samuelson principle in both the weak and strong forms for workable Leontief systems are valid within the context of the nonlinear model.

where y(t) represents the vector of endogenous variables, x(t) the vector of exogenous variables, u(t) the vector of stochastic disturbances, and t the tth period of observation. The matrices A, (T = 0, 1, . . . , m) of the structural coefficients are square matrices of order G. It is assumed that the conditions justifying the theorems in [3, Ch. 10] are satisfied, and that there are no nonlinear restrictions on the elements of A.. The stability of the system is determined by reference to the dominant root of the polynomial equation (2) det E Atmt) =0. t=O

[The limited information maximum likelihood and two-stage least squares estimates have the same asymptotic normal distribution; the ordinary least squares estimate has another asymptotic normal distribution. This paper considers more accurate approximations to the distributions of the so-called "k-class" estimates. An asymptotic expansion of the distribution of such an estimate is given in terms of an Edgeworth or Gram-Charlier series (of which the leading term is the normal distribution). The development also permits expression of the exact distribution in several forms. The distributions of the two-stage least squares and ordinary least squares estimates are transformed to doubly-noncentral F distributions. Numerical comparisons are made between the approximate distributions and exact distributions calculated by the second author.]