Time-series analysis : a comprehensive introduction for social scientists
著者
書誌事項
Time-series analysis : a comprehensive introduction for social scientists
Cambridge University Press, 2009
- : pbk
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注記
"This digitally printed version 2009"--T.p. verso
Bibliography: p. 393-396
Includes index
内容説明・目次
内容説明
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.
目次
- 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.
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