In this article, we present a reference case of mean field games. This case can be seen as a reference for two main reasons. First, the case is simple enough to allow for explicit resolution: Bellman functions are quadratic, stationary measures are normal and stability can be dealt with...

Nous présentons un exemple archétypal de jeu à champ moyen. Cet exemple est important à deux égards. Tout d'abord, il est suffisamment simple pour permettre l'obtention de solutions explicites : les fonctions de Bellman sont quadratiques, les mesures stationnaires gaussiennes et l'étude de...

This paper provides a closed-form solution for the price-dividend ratio in a standard asset pricing model with stochastic volatility. The growth rate of the endowment is a first-order Gaussian autoregression, while the stochastic volatility innovations can be drawn from any distribution for...

Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical...

We provide a Matlab quadratic optimization tool based on Markowitz's citical line algorithm that significantly outperforms standard software packages and a recently developed operations research algorithm. As an illustration: For a 2000 asset universe our method needs less than a second to...

The computational time required to solve and estimate dynamic economic models is one of the main constraints in empirical research. The Endogenous Grid Method (EGM) proposed by Carroll (2006) is known to offer impressive speed gains over more traditional stochastic dynamic programming methods,...

The author explains how optimal-control problems can be solved with a common spreadsheet such as Microsoft Excel. He illustrates the method with several examples ranging from simple models to quite advanced topics. The method is intended to be beneficial to students and teachers working with...

This paper presents a numerical nonlinear dynamic programming algorithm for solving so-called optimal learning or adaptive control problems. These are decision problems with unknown parameters where the decisionmaker updates beliefs by Bayes rule. The updating equations are nonlinear. As a...

This paper introduces a numerical method for solving concave continuous state dynamic programming problems which is based on a pair of polyhedral approximations of concave functions. The method is globally convergent and produces computable upper and lower bounds on the value function which can...

Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical...