Computing the Distributions of Economic Models via Simulation
研究了一种蒙特卡洛算法，用于计算具有马尔可夫性质的随机模型的边际密度和稳态密度，并证明了全局渐近正态性和收敛速度，为经济模型模拟提供了误差界。
We study a Monte Carlo algorithm for computing marginal and stationary densities of stochastic models with the Markov property, establishing global asymptotic normality and OP(n-1/2) convergence. Asymptotic normality is used to derive error bounds in terms of the distribution of the norm deviation.