Prediction, filtering and smoothing in non‐linear and non‐normal cases using Monte Carlo integration
开发了一种基于模拟的非线性滤波器，用于非线性或非正态结构时间序列模型的预测和平滑，通过蒙特卡洛积分推导加权函数的递归算法，实验表明该方法优于数值积分和扩展卡尔曼滤波，并应用于人均最终消费数据估计。
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 procedure (KNI), and (2) SDE and KNI give less biased parameter estimates than the extended Kalman filter (EKF). Finally, an estimation of per capita final consumption data is taken as an application to the non‐linear filtering problem.