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Nonlinear Filters

Estimation and Applications

  • Hisashi¬†Tanizaki

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Hisashi Tanizaki
    Pages 1-13
  3. Hisashi Tanizaki
    Pages 15-41
  4. Hisashi Tanizaki
    Pages 43-69
  5. Hisashi Tanizaki
    Pages 71-111
  6. Hisashi Tanizaki
    Pages 113-173
  7. Hisashi Tanizaki
    Pages 175-203
  8. Hisashi Tanizaki
    Pages 205-231
  9. Hisashi Tanizaki
    Pages 233-243
  10. Back Matter
    Pages 245-255

About this book

Introduction

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.

Keywords

Prognoseverfahren Simulation Zeitreihen econometrics forecasting nichtlineare Filter nonlinear filters time series

Authors and affiliations

  • Hisashi¬†Tanizaki
    • 1
  1. 1.Faculty of EconomicsKobe UniversityRokkodai, NadakuJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-03223-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 1996
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-08253-5
  • Online ISBN 978-3-662-03223-7
  • Buy this book on publisher's site