Nonlinear Filters: Estimation and Applications / Edition 2

Nonlinear Filters: Estimation and Applications / Edition 2

by Hisashi Tanizaki
     
 

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ISBN-10: 3540613269

ISBN-13: 9783540613268

Pub. Date: 08/16/1996

Publisher: Springer Berlin Heidelberg

Nonlinear and nonnormal filters are introduced and developed. Traditional nonlinear filters such as the extended Kalman filter and the Gaussian sum filter give biased filtering estimates, and therefore several nonlinear and nonnormal filters have been derived from the underlying probability density functions. The density-based nonlinear filters introduced in this

Overview

Nonlinear and nonnormal filters are introduced and developed. Traditional nonlinear filters such as the extended Kalman filter and the Gaussian sum filter give biased filtering estimates, and therefore several nonlinear and nonnormal filters have been derived from the underlying probability density functions. The density-based nonlinear filters introduced in this book utilize numerical integration, Monte-Carlo integration with importance sampling or rejection sampling and the obtained filtering estimates are asymptotically unbiased and efficient. By Monte-Carlo simulation studies, all the nonlinear filters are compared. Finally, as an empirical application, consumption functions based on the rational expectation model are estimated for the nonlinear filters, where US, UK and Japan economies are compared.

Product Details

ISBN-13:
9783540613268
Publisher:
Springer Berlin Heidelberg
Publication date:
08/16/1996
Series:
Lecture Notes in Economics and Mathematica
Edition description:
2nd rev. and enlarged ed. 1996
Pages:
256
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Contents: Introduction.- State-Space Model in Linear Case.- Traditional Nonlinear Filters.- Density Based Nonlinear Filters.- Monte-Carlo Experiments.- Application of Ninlinear Filters.- Prediction and Smoothing.- Summary and Concluding Remarks.

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