Prediction, Filtering, and Smoothing in Nonlinear and Nonnormal Cases Using Monte-Carlo Integration

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Mariano, Roberto S., Tanizaki, Hisashi, Van Dijk, Herman, Monfort, Alain, Brown, B.W.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 1994
Subjects:
Economics
Online Access:https://ink.library.smu.edu.sg/soe_research/381
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: 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

Similar Items