Applications of Computer Aided Time Series Modeling

  • Masanao Aoki
  • Arthur M. Havenner

Part of the Lecture Notes in Statistics book series (LNS, volume 119)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Introduction to State Space Modeling

    1. Front Matter
      Pages 1-1
  3. Applications of State Space Algorithm

  4. Applications of Neural Networks

  5. Back Matter
    Pages 237-238

About these proceedings


This book consists of three parts: Part One is composed of two introductory chapters. The first chapter provides an instrumental varible interpretation of the state space time series algorithm originally proposed by Aoki (1983), and gives an introductory account for incorporating exogenous signals in state space models. The second chapter, by Havenner, gives practical guidance in apply­ ing this algorithm by one of the most experienced practitioners of the method. Havenner begins by summarizing six reasons state space methods are advanta­ geous, and then walks the reader through construction and evaluation of a state space model for four monthly macroeconomic series: industrial production in­ dex, consumer price index, six month commercial paper rate, and money stock (Ml). To single out one of the several important insights in modeling that he shares with the reader, he discusses in Section 2ii the effects of sampling er­ rors and model misspecification on successful modeling efforts. He argues that model misspecification is an important amplifier of the effects of sampling error that may cause symplectic matrices to have complex unit roots, a theoretical impossibility. Correct model specifications increase efficiency of estimators and often eliminate this finite sample problem. This is an important insight into the positive realness of covariance matrices; positivity has been emphasized by system engineers to the exclusion of other methods of reducing sampling error and alleviating what is simply a finite sample problem. The second and third parts collect papers that describe specific applications.


DEX Estimator Variance algorithms computer efficiency evaluation matrices modeling network networks production stochastic systems time series unit roots

Editors and affiliations

  • Masanao Aoki
    • 1
  • Arthur M. Havenner
    • 2
  1. 1.Department of EconomicsUniversity of California Los AngelesLos AngelesUSA
  2. 2.Department of Agricultural EconomicsUniversity of California, DavisDavisUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1997
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94751-8
  • Online ISBN 978-1-4612-2252-1
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site