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

Publication Type

Journal Article

Publication Date

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

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

Additional URL

https://doi.org/10.1002/jae.3950090204

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