Discretizing nonlinear, non-Gaussian Markov processes with exact conditional moments
精确匹配条件矩的非线性、非高斯马尔可夫过程离散化
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 maximum entropy. In contrast to existing methods, our approach is not limited to linear Gaussian autoregressive processes. We apply our method to numerically solve asset pricing…