Prediction, Filtering and Smoothing in Non-linear and Non-normal Cases Using Monte Carlo Integration

Hisashi Tanizaki and Roberto Mariano

Journal of Applied Econometrics, 1994, vol. 9, issue 2, 163-79

Abstract: 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. Copyright 1994 by John Wiley & Sons, Ltd.

Date: 1994
References: Add references at CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://links.jstor.org/sici?sici=0883-7252%2819940 ... 0.CO%3B2-1&origin=bc full text (application/pdf)
Access to full text is restricted to JSTOR subscribers. See http://www.jstor.org for details.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jae:japmet:v:9:y:1994:i:2:p:163-79

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is currently edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and Christopher F. Baum ().