If the elements of the choice set in a decision model involving randomness are not arbitrary, but restricted appropriately, an expected utility ordering of them can be represented by a mean standard deviation ranking function. These restrictions can apply to the form of, or can specify...

In this note the method of Hamiltonian dynamics is used to characterize the time-consistent solution to the optimal control problem in a deterministic continuous time rational expectations model. A linear quadratic example based on the work of Miller and Salmon is used for simplicity. To derive...

This note describes an algorithm for the solution of rational expectations models with saddlepoint stability properties. The algorithm is based on the method of multiple shooting, which is widely used to solve mathematically similar problems in the physical sciences. Potential applications to...

Many methods have been proposed for the solution and simulation of medium or large size models under the assumption of rational expectations. The purpose of this paper is to present these methods, and to show how and where each can be applied. The methods fall into two groups. Methods in the...

The distinctiof between predetermined and non-predetermined variables is a crucial one in rational expectations models. I consider and reject two definitions, one proposed by Blanchard and Kahn and one by Chow. Both definitions lead to possible misc1assifications. Instead I propose the following...

The paper investigates the robustness of the proposition that in stochastic models contingent or feddback rules dominate fiped or openloop rules. Four arguments in favour of fixed rules are considere`. 1) The presence of an incompetent op malevolent policy maker. 2) A trade-off between...

The paper analyses a class of two-point boundary value problem for systems of linear differential equations with constant coefficients. The boundary conditions are expressed as linear restrictions on the state vector at an initial time and at a finite terminal time. This is applicable even if...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...

An elementary exposition is presented of a convenient and practical solution procedure for a broad class of linear rational expectations models. The undetermined-coefficient approach utilized keeps the mathematics very simple and permits consideration of alternative solution criteria

We explore alternative approaches to numerical solutions of large rational expectations models. We discuss and compare several current alternatives, focussing on the tradeoffs in accuracy, space, and speed. The models range from representative agent models with many goods and capital stocks, to...