Nonlinear Filters: Estimation and Applications

✍ Scribed by Hisashi Tanizaki (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 1996
• Tongue: English
• Leaves: 263
• Edition: 2
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages I-XIX
Introduction....Pages 1-13
State-Space Model in Linear Case....Pages 15-41
Traditional Nonlinear Filters....Pages 43-69
Density-Based Nonlinear Filters....Pages 71-111
Monte-Carlo Experiments....Pages 113-173
Application of Nonlinear Filters....Pages 175-203
Prediction and Smoothing....Pages 205-231
Summary and Concluding Remarks....Pages 233-243
Back Matter....Pages 245-255

✦ Subjects

Economic Theory; Statistics for Business/Economics/Mathematical Finance/Insurance