Overview
- This is the first and only book on continuous time modeling in the behavioral and related sciences
- Introduces Newtonian dynamics modeling by means of differential equations for behavioral and related sciences
- Covers 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
- The computer code needed for running the analyses is available online
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Table of contents (16 chapters)
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Front Matter
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Back Matter
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
About this book
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, neitherhuman 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.
Editors and Affiliations
About the editors
Bibliographic Information
Book Title: Continuous Time Modeling in the Behavioral and Related Sciences
Editors: Kees van Montfort, Johan H. L. Oud, Manuel C. Voelkle
DOI: https://doi.org/10.1007/978-3-319-77219-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-77218-9Published: 22 October 2018
Softcover ISBN: 978-3-030-08401-1Published: 24 January 2019
eBook ISBN: 978-3-319-77219-6Published: 11 October 2018
Edition Number: 1
Number of Pages: XI, 442
Number of Illustrations: 51 b/w illustrations, 44 illustrations in colour
Topics: Statistics for Life Sciences, Medicine, Health Sciences, Behavioral Sciences, Computer Appl. in Social and Behavioral Sciences, Statistics for Social Sciences, Humanities, Law, Biostatistics