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...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
1994
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/soe_research/381 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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