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Continuous Time Modeling in the Behavioral and Related Sciences

  • Kees van Montfort
  • Johan H.L. Oud
  • Manuel C. Voelkle

Table of contents

  1. Front Matter
    Pages i-xi
  2. Johan H. L. Oud, Manuel C. Voelkle, Charles C. Driver
    Pages 1-26
  3. Oisín Ryan, Rebecca M. Kuiper, Ellen L. Hamaker
    Pages 27-54
  4. Nynke M. D. Niezink, Tom A. B. Snijders
    Pages 111-134
  5. Joel S. Steele, Joseph E. Gonzales, Emilio Ferrer
    Pages 135-162
  6. Pascal R. Deboeck, Kristopher J. Preacher, David A. Cole
    Pages 179-203
  7. Meng Chen, Sy-Miin Chow, Michael D. Hunter
    Pages 205-238
  8. Steven M. Boker, Stacey S. Tiberio, Robert G. Moulder
    Pages 239-258
  9. Andreas M. Brandmaier, Charles C. Driver, Manuel C. Voelkle
    Pages 259-282
  10. Omar Licandro, Luis A. Puch, Jesús Ruiz
    Pages 283-303
  11. Siem Jan Koopman, Jacques J. F. Commandeur, Frits D. Bijleveld, Sunčica Vujić
    Pages 305-315
  12. Marcus J. Chambers, J. Roderick McCrorie, Michael A. Thornton
    Pages 317-357
  13. Back Matter
    Pages 437-442

About this book

Introduction

This unique book provides an overview of continuous time modeling in the behavioral and related sciences. It argues that the use of discrete time models for processes that are in fact evolving in continuous time produces problems that make their application in practice highly questionable. One main issue is the dependence of discrete time parameter estimates on the chosen time interval, which leads to incomparability of results across different observation intervals. Continuous time modeling by means of differential equations offers a powerful approach for studying dynamic phenomena, yet the use of this approach in the behavioral and related sciences such as psychology, sociology, economics and medicine, is still rare. This is unfortunate, because in these fields often only a few discrete time (sampled) observations are available for analysis (e.g., daily, weekly, yearly, etc.). However, as emphasized by Rex Bergstrom, the pioneer of continuous-time modeling in econometrics, neither human beings nor the economy cease to exist in between observations.

In 16 chapters, the book addresses a vast range of topics in continuous time modeling, from approaches that closely mimic traditional linear discrete time models to highly nonlinear state space modeling techniques. Each chapter describes the type of research questions and data that the approach is most suitable for, provides detailed statistical explanations of the models, and includes one or more applied examples. To allow readers to implement the various techniques directly, accompanying computer code is made available online. The book is intended as a reference work for students and scientists working with longitudinal data who have a Master's- or early PhD-level knowledge of statistics.

Keywords

37N40, 62M10, 62P15, 62P25, 65F60, 91B99, 91D30, 93E99, 97M70 continuous time modeling recursive partitioning analysis of panel data structural equation modeling longitudinal studies Time series data Panel data State space modeling CARMA modeling structural equation modeling impulse response exact discrete time model adaptive equilibrium time-varying parameters Bayesian continuous time modeling

Editors and affiliations

  • Kees van Montfort
    • 1
  • Johan H.L. Oud
    • 2
  • Manuel C. Voelkle
    • 3
  1. 1.Marketing and Supply Chain ManagementNyenrode Business UniversityBreukelenThe Netherlands
  2. 2.Behavioural Science InstituteUniversity of NijmegenNijmegenThe Netherlands
  3. 3.Department of PsychologyHumboldt-Universität zu BerlinBerlinGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-77219-6
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-77218-9
  • Online ISBN 978-3-319-77219-6
  • Buy this book on publisher's site
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