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Look Inside Time-Series Analysis

Time-Series Analysis
A Comprehensive Introduction for Social Scientists


  • Date Published: March 2009
  • availability: Available
  • format: Paperback
  • isbn: 9780521103367

£ 42.99

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About the Authors
  • 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.

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    Product details

    • Date Published: March 2009
    • format: Paperback
    • isbn: 9780521103367
    • length: 420 pages
    • dimensions: 229 x 152 x 24 mm
    • weight: 0.61kg
    • availability: Available
  • Table of Contents

    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

  • Author

    John M. Gottman

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