Restricted Kalman Filtering_ Theory, Methods, and Application-

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(SpringerBriefs in Statistics 12) Adrian Pizzinga (auth.)-Restricted Kalman Fil.pdf (717.8 KB, 需要: 10 个论坛币)

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(SpringerBriefs in Statistics 12)
Adrian Pizzinga  (auth.)-
Restricted Kalman Filtering_ Theory, Methods, and Application-
Springer New York (2012)

ISSN 2191-544X ISSN 2191-5458 (electronic)
ISBN 978-1-4614-4737-5 ISBN 978-1-4614-4738-2 (eBook)
DOI 10.1007/978-1-4614-4738-2
Springer New York Heidelberg Dordrecht London
Library of Congress Control Number: 2012941620
© Springer Science+Business Media New York 2012
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilms or in any other physical way, and transmission or information
storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology
now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection
with reviews or scholarly analysis or material supplied specifically for the purpose of being entered
and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of
this publication or parts thereof is permitted only under the provisions of the Copyright Law of the
Publisher’s location, in itscurrent version, and permission for use mustalways be obtained from Springer.
Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations
are liable to prosecution under the respective Copyright Law.
Theuseofgeneral descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
While the advice and information in this book are believed to be true and accurate at the date of
publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for
any errors or omissions that may be made. The publisher makes no warranty, express or implied, with
respect to the material contained herein.
Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)
Contents
1 Introduction ................................................................... 1
1.1 Motivation ................................................................ 1
1.2 A Glimpse at the Literature.............................................. 2
1.2.1 Statistics Papers.................................................. 2
1.2.2 Engineering Papers .............................................. 3
1.3 The Book’s Contents..................................................... 4
1.4 Organization.............................................................. 5
2 Linear State Space Models and Kalman Filtering ........................ 7
2.1 The Model ................................................................ 7
2.2 Kalman Equations........................................................ 7
2.3 Introducing Linear Restrictions ......................................... 8
3 Restricted Kalman Filtering: Theoretical Issues .......................... 11
3.1 Augmented Restricted Kalman Filtering: Alternative Proofs.......... 11
3.1.1 Geometrical Proof ............................................... 11
3.1.2 Computational Proof ............................................ 13
3.1.3 Conditional Expectation Proof .................................. 15
3.2 Statistical Efficiency ..................................................... 16
3.3 Restricted Kalman Filtering Versus Restricted Recursive
Least Squares............................................................. 18
3.4 Initialization .............................................................. 21
3.4.1 Motivation........................................................ 21
3.4.2 Reviewing the Initial Exact Kalman Smoother................. 21
3.4.3 Combining Exact Initialization with Linear Restrictions ...... 22
4 Restricted Kalman Filtering: Methodological Issues ..................... 27
4.1 Random-Walk State Vectors Under Time-Invariant Restrictions ...... 27
4.2 Reduced Restricted Kalman Filtering................................... 28
4.2.1 Motivation........................................................ 28
4.2.2 The Method ...................................................... 29
4.2.3 Reducing Versus Augmenting................................... 30
4.3 Predictions from a Restricted State Space Model ...................... 32
vii
viii Contents
5 Applications ................................................................... 35
5.1 Case I: Semistrong Dynamic Style Analysis ........................... 36
5.1.1 Motivation........................................................ 36
5.1.2 Competing Models .............................................. 37
5.1.3 Model Selection.................................................. 39
5.1.4 Empirical Results ................................................ 40
5.2 Case II: Estimation of Dynamic Exchange-Rate Pass-Through ....... 44
5.2.1 Motivation........................................................ 44
5.2.2 Empirical Results ................................................ 46
5.3 Case III: GDP Benchmarking Estimation and Prediction.............. 51
5.3.1 Motivation........................................................ 51
5.3.2 Model Setup...................................................... 51
5.3.3 Empirical Results ................................................ 52
6 Further Extensions ........................................................... 53
References.......................................................................... 55

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