Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-1994
Abstract
A simulation-based non-linear filter is developed for prediction and smoothing in non-linear and/or nonnormal 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.
Discipline
Econometrics | Economics
Research Areas
Econometrics
Publication
Journal of Applied Econometrics
Volume
9
Issue
2
First Page
163
Last Page
179
ISSN
0883-7252
Identifier
10.1002/jae.3950090204
Publisher
Wiley
Citation
Tanizaki, Hisashi and Mariano, Roberto S..
Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration. (1994). Journal of Applied Econometrics. 9, (2), 163-179.
Available at: https://ink.library.smu.edu.sg/soe_research/373
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1002/jae.3950090204