This paper presents stochastic simulations of a non-linear Phillips curve model with a random shock on the labor market, a random shock on inflation, and 20 state variables to represent a rather complex dynamical adjustment. Various methods are used to perform the simulations: two approaches to...

We propose to apply to the simulation of general nonlinear rational-expectation models a method where the expectation functions are approximated through a higher-order Taylor expansion. This method has been advocated by Judd (1998) and others for the simulation of stochastic optimal-control...

This paper investigates the accuracy of a perturbation method in approximating the solution to stochastic equilibrium models under rational expectations. As a benchmark model, we use a version of asset pricing models proposed by Burnside [1988] which admits a closed-form solution while not...

Since the contribution of Kydland and Prescott (1977), it is well known that the optimal Ramsey policy is time inconsistent. In a series of recent contributions, Woodford (2003) proposes a new methodology to circumvent this problem, namely the timeless perspective solution. However, one main...

A recent literature have proposed different methods to produce second-order accurate approximation to the solutions to DGSE's from a straightforward second-order approximation of the model. Among others, Judd (2002), Jin and Judd (2002) show how to compute approximation of arbitrary order on...

DYNARE: A program for the simulation of rational expectation models Michel Juillard (CEPREMAP and University Paris 8) DYNARE is a user oriented general program for the simulation of deterministic or stochastic models. For linear models, it implements a generalized Schur decomposition algorithm;...

In this paper we investigate theoretically the numerical bias due to the truncation of structurally infinite time forward-looking models, by the means of various terminal conditions. On a general multivariate optimal growth model, we first analytically confirm some well-known heuristic...