NON‐LINEAR DSGE MODELS AND THE CENTRAL DIFFERENCE KALMAN FILTER
提出一种基于中心差分卡尔曼滤波的准最大似然方法，用于估计非线性DSGE模型，允许非高斯冲击，并在蒙特卡洛研究中验证了估计量的一致性和渐近正态性。
SUMMARY This paper introduces a quasi maximum likelihood approach based on the central difference Kalman filter to estimate non‐linear dynamic stochastic general equilibrium (DSGE) models with potentially non‐Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models solved up to third order. These properties are verified in a Monte Carlo study for a DSGE model solved to second and third order with structural shocks that are Gaussian, Laplace distributed, or display stochastic volatility. Copyright © 2012 John Wiley & Sons, Ltd.