[A choice function, which maps each set of alternatives in a domain of feasible sets into a non-empty subset of itself (called the choice set), is said to be representable by a weak order if some weak order on the alternatives has maximum elements within each feasible set, all of which are in the choice set of that feasible set. A Partial Congruence Axiom ("every non-empty finite collection of feasible sets has an alternative which is in the choice set of every feasible set in the collection which contains that alternative") is shown to be necessary and sufficient for weak order representability when all choice sets are finite. A stronger form of partial congruence is proved to be necessary and sufficient for weak order representability when the number of feasible sets is countable, regardless of the cardinalities of the choice sets. The general case of arbitrary cardinalities for the domain and the choice sets is presently unsettled.]

An approximate solution, based on the method of dynamic programming, is provided for the optimal control of a system of nonlinear structural equations in econometrics with unknown parameters using a quadratic loss function. It generalizes the methods previously proposed by the author for the control of a nonlinear econometric model with constant parameters and of a linear econometric model with uncertain parameters. It is an improvement over the method of certainty equivalence which replaces the unknown parameters by their mathematical expectations and utilizes the solution for the resulting model. Since the solution is given in the form of feedback control equations, many of the useful concepts and techniques developed in the theory of optimal feedback control for linear systems are now applicable to the control of nonlinear systems using the method proposed, including the calculation of the expected loss of the system under control by analytical rather than Monte Carlo techniques. IN THIS PAPER, I present an approximate solution to the optimal control of a system of nonlinear structural equations using a quadratic welfare loss function when the parameters of the system are unknown. This is a generalization of ths solution given in Chapter 12 of Chow [2] for the control of nonlinear econometric systems with known parameters. It is also a generalization of the solution given in Chow [1] for the control of linear econometric systems with unknown parameters. The method of dynamic programming is applied to solve an optimal control problem involving a nonlinear econometric system with unknown parameters. As it turns out, the solution amounts to linearizing the nonlinear model about some nearly optimal control solution path and then applying a method for controlling the resulting linear model with uncertain parameters. This paper advances the state of the art in the control of nonlinear econometric systems as it improves upon the certainty-equivalence solution which is obtained by replacing the random parameters in a system by their mathematical expectations. It provides for a set of numerical feedback control equations based on a system of nonlinear structural equations in econometrics. It will show that many useful analytical concepts and tools developed in the theory of control of linear systems are indeed applicable to the control of nonlinear systems. Furthermore, in the derivation of an approximate solution using the method of dynamic programming, it will indicate precisely where the approximation takes place and why an exact solution is difficult to achieve. In Section 2, we set up the control problem and provide an exact solution to the optimal control problem for the last period. In Section 3, we give an approximate solution to the multiperiod control problem using dynamic programming. In Section 4, the mathematical expectations required in the solution of Section 3

[This paper presents conditions on the coefficients of Nth order linear constant coefficient functional equations in generalized differences, necessary and sufficient for asymptotic stability. These conditions are analogous to the Schur-Cohn conditions for difference equations in dated form, and to the Routh-Hurwitz conditions for differential equations.]

A multiple equation nonlinear regression model with serially independent disturbances is considered. The estimation of the parameters in this model by maximum likelihood and minimum distance methods is discussed and our main subject is the relationship between these procedures. We establish that if the number of observations in a sample is sufficiently large, the iterated minimum distance procedure converges almost surely and the limit of this sequence of iterations is the quasi-maximum likelihood estimator.

[This paper develops a simulation model to study the income distribution effects--total and factorial-of optimum restrictions on the flows of factors and products across national boundaries. Imposing both optimum tariffs and optimum taxes on factor flows allows an increase in national income that is much larger than the sum of the two effects evaluated separately. Often there are large shifts in the incomes of factors even though total income changes only slightly.]

[In this paper we consider the estimation of a model with time varying structure. The parameters of the model are assumed to be subject to permanent and transitory changes over time. Estimation methods are developed, and the asymptotic properties of the estimates are derived.]

[A regression model in which the disturbances exhibit a certain type of heteroscedasticity is considered. Maximum likelihood methods of estimation are developed and compared with the two-step estimation procedure. A likelihood ratio test for heteroscedasticity is suggested.]