Computing the Distributions of Economic Models via Simulation
研究一种蒙特卡洛算法，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，建立了全局渐近正态性和OP(n–1/2)收敛性，并利用渐近正态性推导了范数偏差分布下的误差界。
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.