Prediction, filtering and smoothing in non‐linear and non‐normal cases using Monte Carlo integration
利用蒙特卡洛积分进行非线性、非正态情形下的预测、滤波与平滑
Abstract A simulation‐based non‐linear filter is developed for prediction and smoothing in non‐linear and/or non‐normal structural time‐series models. Recursive algorithms of weighting functions are derived by applying Monte Carlo integration. Through Monte Carlo experiments, it is shown that (1) for a small number of random draws (or nodes) our simulation‐based density estimator using Monte Carlo integration (SDE) performs better than Kitagawa's numerical integration…