Time-Series Analysis: A Comprehensive Introduction for Social Scientists

by John M. Gottman

Overview

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.

Product Details

ISBN-13:	9780521103367
Publisher:	Cambridge University Press
Publication date:	03/19/2009
Pages:	420
Product dimensions:	5.98(w) x 9.02(h) x 0.94(d)

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.