We present an algorithm and software routines for computing nth-order approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment (and from a ``timeless...

The Anderson-Moore algorithm is a powerful method for solving linear saddle-point models. The algorithm has proved useful in a wide variety of applications, including analyzing linear perfect-foresight models and providing initial solutions and asymptotic constraints for nonlinear models. The...

We present an algorithm and software routines for computing nth-order approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment in an optimizing-agent model...

This paper describes an extension of the stack algorithm of Julliard, which makes it possible to (a) use any linear terminal condition, and (b) compute fixed points with the algorithm. Numerical results are presented for solving three different macroeconomic models including the Canada Model....

Anderson's papers describe a method for solving linear saddle point models. The numerical implementation of the algorithm has proved useful in a wide array of applications including analyzing linear perfect foresight models, and providing initial solutions and asymptotic constraints for...

Anderson & Moore describe a powerful method for solving linear saddle point models. The algorithm has proved useful in a wide array of applications including analyzing linear perfect foresight models, providing initial solutions and asymptotic constraints for nonlinear models. However, many...