Restricted Kalman Filtering

Theory, Methods, and Application

  • Adrian Pizzinga

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST, volume 12)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Adrian Pizzinga
    Pages 1-5
  3. Adrian Pizzinga
    Pages 35-52
  4. Adrian Pizzinga
    Pages 53-54
  5. Back Matter
    Pages 55-57

About this book


​​​​​​​​ ​In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone.  This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where the proposed methods are illustrated and evaluated. The Brief has a short chapter on linear state space models and the Kalman filter, aiming to make the book self-contained and to give a quick reference to the reader (notation and terminology). The prerequisites would be a contact with time series analysis in the level of Hamilton (1994) or Brockwell & Davis (2002) and also with linear state models and the Kalman filter – each of these books has a chapter entirely dedicated to the subject. The book is intended for graduate students, researchers and practitioners in statistics (specifically: time series analysis and econometrics).


Econometrics General linear models Kalman Filter

Authors and affiliations

  • Adrian Pizzinga
    • 1
  1. 1.Fluminense Federal University, Department of StatisticsInstitute of Mathematics & StatisticsRio de JaneiroBrazil

Bibliographic information