Add to ORKGAbstract. This paper presents a computationally fesible procedure for the optimal control and stochastic simulation of large nonlinear models with rational expectations under the assumption of certainty equivalence. Key words: optimal control, stochastic simulation, rational expectations 1
This paper presents a stochastic model predictive control approach for nonlinear systems subject to ...
We study optimal stochastic control problems under model uncertainty. We rewrite such problems as (z...
This paper develops methods to solve for optimal discretionary policies and optimal commitment polic...
A computationally feasible method for the full information maximum-likelihood estimation of models w...
This thesis presents a comprehensive set of techniques for solving, simulating, analysing and contro...
This paper develops the Parameterized Expectations Approach (PEA) for solving nonlinear dynamic stoc...
A computationally feasible method for the full information maximum likelihood estimation of models w...
A number of emerging applications in the field of optimal control theory require the computation of ...
In this paper, we present a method for using rational expectations in a stochastic linear-quadratic ...
Since the onset of the rational expectations revolution in macroeconomics some 30 or more years ago,...
Since the onset of the rational expectations revolution in macroeconomics some 30 or more years ago,...
In the financial engineering field, many problems can be formulated as stochastic control problems. ...
This paper presents new, computationally efficient algorithms for solution and estimation of nonline...
We address the design of optimal control strategies for high-dimensional stochastic dynamical system...
The ultimate aim in building an econometric model is always, at least implicitly, to develop a tool ...
This paper presents a stochastic model predictive control approach for nonlinear systems subject to ...
We study optimal stochastic control problems under model uncertainty. We rewrite such problems as (z...
This paper develops methods to solve for optimal discretionary policies and optimal commitment polic...
A computationally feasible method for the full information maximum-likelihood estimation of models w...
This thesis presents a comprehensive set of techniques for solving, simulating, analysing and contro...
This paper develops the Parameterized Expectations Approach (PEA) for solving nonlinear dynamic stoc...
A computationally feasible method for the full information maximum likelihood estimation of models w...
A number of emerging applications in the field of optimal control theory require the computation of ...
In this paper, we present a method for using rational expectations in a stochastic linear-quadratic ...
Since the onset of the rational expectations revolution in macroeconomics some 30 or more years ago,...
Since the onset of the rational expectations revolution in macroeconomics some 30 or more years ago,...
In the financial engineering field, many problems can be formulated as stochastic control problems. ...
This paper presents new, computationally efficient algorithms for solution and estimation of nonline...
We address the design of optimal control strategies for high-dimensional stochastic dynamical system...
The ultimate aim in building an econometric model is always, at least implicitly, to develop a tool ...
This paper presents a stochastic model predictive control approach for nonlinear systems subject to ...
We study optimal stochastic control problems under model uncertainty. We rewrite such problems as (z...
This paper develops methods to solve for optimal discretionary policies and optimal commitment polic...