Efficient Likelihood Evaluation of State-Space Representations
开发了一种数值方法，用于高效评估非线性非高斯状态空间模型的似然，通过连续近似滤波密度和优化重要性抽样，得到参数连续函数形式的似然近似，便于参数估计，并应用于动态随机一般均衡模型。
We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure employs continuous approximations of filtering densities, and delivers unconditionally optimal global approximations of targeted integrands to achieve likelihood approximation. Optimized approximations of targeted integrands are constructed via efficient importance sampling. Resulting likelihood approximations are continuous functions of model parameters, greatly enhancing parameter estimation. We illustrate our procedure in applications to dynamic stochastic general equilibrium models. Copyright 2013, Oxford University Press.