Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| 格式: | text |
| 語言: | English |
| 出版: |
Institutional Knowledge at Singapore Management University
1994
|
| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/soe_research/381 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Singapore Management University |
| 語言: | English |
| id |
sg-smu-ink.soe_research-1380 |
|---|---|
| record_format |
dspace |
| spelling |
sg-smu-ink.soe_research-13802010-09-23T05:48:03Z Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration Mariano, Roberto S. Tanizaki, Hisashi Van Dijk, Herman Monfort, Alain Brown, B.W. 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. 1994-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soe_research/381 info:doi/10.1002/jae.3950090204 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Economics |
| institution |
Singapore Management University |
| building |
SMU Libraries |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
SMU Libraries |
| collection |
InK@SMU |
| language |
English |
| topic |
Economics |
| spellingShingle |
Economics Mariano, Roberto S. Tanizaki, Hisashi Van Dijk, Herman Monfort, Alain Brown, B.W. Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration |
| description |
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. |
| format |
text |
| author |
Mariano, Roberto S. Tanizaki, Hisashi Van Dijk, Herman Monfort, Alain Brown, B.W. |
| author_facet |
Mariano, Roberto S. Tanizaki, Hisashi Van Dijk, Herman Monfort, Alain Brown, B.W. |
| author_sort |
Mariano, Roberto S. |
| title |
Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration |
| title_short |
Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration |
| title_full |
Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration |
| title_fullStr |
Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration |
| title_full_unstemmed |
Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration |
| title_sort |
prediction, filtering, and smoothing in nonlinear and nonnormal cases using monte-carlo integration |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
1994 |
| url |
https://ink.library.smu.edu.sg/soe_research/381 |
| _version_ |
1770569144233623552 |