Time-series analysis : a comprehensive introduction for social scientists

Bibliographic Information

Time-series analysis : a comprehensive introduction for social scientists

John M. Gottman

Cambridge University Press, 2009

  • : pbk

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Note

"This digitally printed version 2009"--T.p. verso

Bibliography: p. 393-396

Includes index

Description and Table of Contents

Description

Since the 1970s social scientists and scientists in a variety of fields - psychology, sociology, education, psychiatry, economics and engineering - have been interested in problems that require the statistical analysis of data over time and there has been in effect a conceptual revolution in ways of thinking about pattern and regularity. This book is a comprehensive introduction to all the major time-series techniques, both time-domain and frequency-domain. It includes work on linear models that simplify the solution of univariate and multivariate problems. The author begins with a non-mathematical overview: throughout, he provides easy-to-understand, fully worked examples drawn from real studies in psychology and sociology. Other, less comprehensive, books on time-series analysis require calculus: this presupposes only a standard introductory statistics course covering analysis of variance and regression. The chapters are short, designed to build concepts (and the reader's confidence) one step at a time. Many illustrations aid visual, intuitive understanding. Without compromising mathematical rigour, the author keeps in mind the reader who does no have an easy time with mathematics: the result is a readily accessible and practical text.

Table of Contents

  • Preface
  • Part I. Overview: 1. The search for hidden structures
  • 2. The ubiquitous cycles
  • 3. How Slutzky created order from chaos
  • 4 Forecasting: Yule's autoregressive models
  • 5. Into the black box with white light
  • 6. Experimentation and change
  • Part II. Time-series models: 7. Models and the problem of correlated data
  • 8. An introduction to time-series models: stationarity
  • 9. What if the data are not stationary?
  • Part III. Deterministic and nondeterministic components: 10. Moving-average models
  • 11. Autoregressive models
  • 12. The complex behaviour of the second-order autoregressive process
  • 13. The partial autocorrelation function: completing the duality
  • 14. The duality of MA and AR processes
  • Part IV. Stationary frequency-domain models: 15. The spectral density function
  • 16. The periodogram
  • 17. Spectral windows and window carpentry
  • 18. Explanation of the Slutzky effect
  • Part V. Estimation in the time domain: 19. AR model fitting and estimation
  • 20. Box-Jenkins model fitting: the ARIMA models
  • 21. Forecasting
  • 22. Model fitting: worked example
  • Part VI. Bivariate time-series analysis: 23. Bivariate frequency-domain analysis
  • 24. Bivariate frequency example: mother-infant play
  • 25. Bivariate time-domain analysis
  • Part VII. Other Techniques: 26. The interrupted time-series experiment
  • 27. Multivariate approaches
  • Notes
  • References
  • Index.

by "Nielsen BookData"

Details

  • NCID
    BA90746434
  • ISBN
    • 9780521103367
  • Country Code
    uk
  • Title Language Code
    eng
  • Text Language Code
    eng
  • Place of Publication
    Cambridge
  • Pages/Volumes
    xvi, 400 p.
  • Size
    23 cm
  • Classification
  • Subject Headings
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