Introduction to Time Series Analysis and Forecasting With Applications of SAS and SPSS

About this book

Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages - SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for common time series models to attain adequate statistical power. To enhance the book's value as a teaching tool, the data sets and programs used in the book are made available on the Academic Press Web site. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical packages makes it easy for the user to properly apply these techniques. It describes principal approaches to time series analysis and forecasting. It presents examples from public opinion research, policy analysis, political science, economics, and sociology. The free Web site contains the data used in most chapters, facilitating learning. The math level is pitched to general social science usage. The glossary makes the material accessible for readers at all levels.

Contents

Preface. Introduction and Overview. Extrapolative Models and Decomposition Models. Introduction to Box-Jenkins Time Series Analysis. The Basic ARIMA Model. Seasonal ARIMA Models. Estimation and Diagnosis. Metadiagnosis and Forecasting. Intervention Analysis. Transfer Function Models. Autoregressive Error Models. A Review of Model and Forecast Evaluation. M. McGee, Power Analysis and Sample Size Determination for Well-Known Time Series Models. Appendix A. Glossary. Index.

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Biography

Robert A. Yaffee, Ph.D., is a Senior Research Consultant/Statistican in the Statistics and Social Science Group of New York University's Academic Computing Facility as well as a Research Scientist/Statistician at the State University of New York Health Science Center in Brooklyn's Division of Geriatric Psychiatry. He received his Ph.D. in political science from Graduate Faculty of Political and Social Research of The New School for Social Research. He serves as a member of the editorial board of the Journal of Gambling Behavior and was on the Research Faculty of Columbia University's School of Public Health before coming to NYU. He also taught in the Statistical packages in the Computer Science Department and the Empirical Research and Advanced Statistics in the Sociology Department of Hunter College. He has published in the fields of statistics, medical research, and psychology. Monnie McGee, Ph.D. is an Assistant Professor of Mathematics and Statistics at Hunter College. She received her Ph.D. from Rice University and has worked as a bio-statistical consultant for The Rockefeller University and as a computational statistician for Electricite de France.