Approximating stochastic processes by finite-state Markov chains is useful for reducing computational complexity when solving dynamic economic models. We provide a new method for accurately discretizing general Markov processes by matching low order moments of the conditional distributions using...

I investigate whether the popular Krusell and Smith algorithm used to solve heterogeneousagent economies with aggregate uncertainty and incomplete markets is likely to be subject to multiple self-fulfilling equilibria. In a benchmark economy, the parameters representing the equilibrium aggregate...

I investigate whether the popular Krusell and Smith algorithm used to solve heterogeneousagent economies with aggregate uncertainty and incomplete markets is likely to be subject to multiple self-fulfilling equilibria. In a benchmark economy, the parameters representing the equilibrium aggregate...

Considers the solution of large systems of linear equations, such as those arising from a large linearized economic model. Recommends (a) that matrix inversion be avoided, in favour of LU methods; and (b) that sparsity in the system be exploited using special computer subroutines. Some examples...

I investigate whether the popular Krusell and Smith algorithm used to solve heterogeneous-agent economies with aggregate uncertainty and incomplete markets is likely to be subject to multiple self-fulfilling equilibria. In a benchmark economy, the parameters representing the equilibrium...

We introduce a ''new'' algorithm that can be used to solve stochastic dynamic general equilibrium models. This approach exploits the fact that the equations defining equilibrium can be viewed as set of algebraic equations in the neighborhood of the steady-state. Then a recursive scheme, which...

This paper describes in detail the algorithm implemented in Dynare for computing the first order approximated solution of a nonlinear rational expectations model. The core of the algorithm is a generalized Schur decomposition (also known as the QZ decomposition), as advocated by several authors...

We introduce a new algorithm that can be used to solve stochastic dynamic general equilibrium models. This approach exploits the fact that the equations defining equilibrium can be viewed as a set of differential algebraic equations in the neighborhood of the steady-state. Then a modified...

Local (perturbation) methods compute solutions in one point and tend to deliver far lower accuracy levels than global solution methods. We develop a hybrid method that solves for some policy functions locally (using a perturbation method) and that solves for the other policy functions globally...