This paper describes a method for solving heterogeneous agent models with aggregate risk and many idiosyncratic states formulated in discrete time. It extends the method proposed by Reiter (2009) and complements recent work by Ahn, Kaplan, Moll, Winberry, and Wolf (2017) on how to solve such...

The "histogram method" (Young, 2010), while the standard approach for analyzing distributional dynamics in heterogeneous agent models, is linear in optimal policies. We introduce a novel method that captures nonlinearities of distributional dynamics. This method solves the distributional...

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...

We study the statistical properties of heterogeneous agent models. Using aBewley-Hugget-Aiyagari model we compute the density function of wealth and in-come and use it for likelihood inference. We study the finite sample properties of themaximum likelihood estimator (MLE) using Monte Carlo...

We review heterogeneous agent-based models of financial stability and their application in stress tests. In contrast to the mainstream approach, which relies heavily on the rational expectations assumption and focuses on situations where it is possible to compute an equilibrium, this approach...

In this paper, we study the statistical properties of heterogeneous agent models with incomplete markets. Using a Bewley-Hugget-Aiyagari model we compute the equilibrium density function of wealth and show how it can be used for likelihood inference. We investigate the identifiability of the...

Non-stationary income processes are standard in quantitative life-cycle models, prompted by the observation that within-cohort income inequality increases with age. This paper generalizes Tauchen (1986) and Rouwenhorst's (1995) discretization methods to non-stationary AR(1) processes. We...

Discrete-time stochastic games with a finite number of states have been widely applied to study the strategic interactions among forward-looking players in dynamic environments. These games suffer from a “curse of dimensionality” when the cost of computing players’ expectations over all...

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...