This paper proposes a simple iterative method – time iteration – to solve linear rational expectation models. I prove that this method converges to the solution with the smallest eigenvalues in absolute value, and provide the conditions under which this solution is unique. In particular, if conditions similar to those of Blanchard and Kahn (1980) are met, the procedure converges to the unique stable solution. Apart from its transparency and simplicity of implementation, the method provides a straightforward approach to solving models with less standard features, such as regime switching models. For large-scale problems the method is 10-20 times faster than existing solution methods
We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an ...
This paper presents new, computationally efficient algorithms for solution and estimation of nonline...
This thesis presents a comprehensive set of techniques for solving, simulating, analysing and contro...
Value function iteration is one of the Standard tools for the solution of dynamic general equilibriu...
This paper describes a set of algorithms for quickly and reliably solving linear rational expectatio...
While rational expectations models with time–varying (random) coefficients have gained some esteem, ...
The paper presents a general solution method for rational expectations models that can be represente...
A solution method is derived in this paper for solving a system of linear rationalexpectations equat...
Caption title.Includes bibliographical references (p. 12-13).Supported by the NSF. CCR-9103804by Dim...
In this chapter, we present theoretical foundations of main methods solving rational expectations mo...
This paper has developed a solution algorithm for linear rational expectation models under imperfect...
AbstractThis paper describes a way of approximating the optimal extrapolation of iterative technique...
We provide a summarized presentation of solution methods for rational expectations models, based on ...
The thesis deals with linear approaches to the Markov Decision Process (MDP). In particular, we desc...
This paper presents algorithms that upper-bound the peak value of a state function along trajectorie...
We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an ...
This paper presents new, computationally efficient algorithms for solution and estimation of nonline...
This thesis presents a comprehensive set of techniques for solving, simulating, analysing and contro...
Value function iteration is one of the Standard tools for the solution of dynamic general equilibriu...
This paper describes a set of algorithms for quickly and reliably solving linear rational expectatio...
While rational expectations models with time–varying (random) coefficients have gained some esteem, ...
The paper presents a general solution method for rational expectations models that can be represente...
A solution method is derived in this paper for solving a system of linear rationalexpectations equat...
Caption title.Includes bibliographical references (p. 12-13).Supported by the NSF. CCR-9103804by Dim...
In this chapter, we present theoretical foundations of main methods solving rational expectations mo...
This paper has developed a solution algorithm for linear rational expectation models under imperfect...
AbstractThis paper describes a way of approximating the optimal extrapolation of iterative technique...
We provide a summarized presentation of solution methods for rational expectations models, based on ...
The thesis deals with linear approaches to the Markov Decision Process (MDP). In particular, we desc...
This paper presents algorithms that upper-bound the peak value of a state function along trajectorie...
We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an ...
This paper presents new, computationally efficient algorithms for solution and estimation of nonline...
This thesis presents a comprehensive set of techniques for solving, simulating, analysing and contro...