Iterative and Recursive Estimation in Structural Nonadaptive Models
提出一种称为潜变量回补的推断方法，适用于结构关系由未观测状态变量和未知参数定义的非线性状态空间模型，通过迭代或递归的EM类策略估计参数，对潜变量回归和动态均衡模型尤其有用。
An inference method, called latent backfitting, is proposed. This method appears well suited for econometric models where the structural relationships of interest define the observed endogenous variables as a known function of unobserved state variables and unknown parameters. This nonlinear state-space specification paves the way for iterative or recursive EM-like strategies. In the E steps, the state variables are forecasted given the observations and a value of the parameters. In the M steps, these forecasts are used to deduce estimators of the unknown parameters from the statistical model of latent variables. The proposed iterative/recursive estimation is particularly useful for latent regression models and for dynamic equilibrium models involving latent state variables. Practical implementation issues are discussed through the example of term structure models of interest rates.