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Prediction, filtering and smoothing in non‐linear and non‐normal cases using Monte Carlo integration
Author(s) -
Tanizaki H.,
Mariano R. S.
Publication year - 1994
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.3950090204
Subject(s) - monte carlo method , estimator , monte carlo integration , smoothing , weighting , extended kalman filter , kalman filter , filter (signal processing) , series (stratigraphy) , algorithm , computer science , quasi monte carlo method , numerical integration , mathematics , mathematical optimization , hybrid monte carlo , statistics , markov chain monte carlo , medicine , paleontology , mathematical analysis , biology , computer vision , radiology
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.