مقدمه‌ای بر تحلیل و پیش‌بینی سری‌های زمانی: با کاربردهای SAS® و SPSS®

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. Key Features * Describes principal approaches to time series analysis and forecasting * Presents examples from public opinion research, policy analysis, political science, economics, and sociology * Free Web site contains the data used in most chapters, facilitating learning * Math level pitched to general social science usage * Glossary makes the material accessible for readers at all levels Audience: Upper level undergraduate and graduate students, professors, and researchers studying: time series analysis and forecasting; longitudinal quantitative analysis; and quantitative policy analysis. Students, professors and researchers in the social sciences, business, management, operations research, engineering, and applied mathematics. This introductory exploration of the principal approaches to time series and forecasting processes explains theories and applications for the social science student. Professor Yaffee devotes significant space to a discussion of how we conceptualize, model, and forecast. He also examines text, model, and forecast evaluation and comparison, and he emphasizes time series programming techniques. Readers can investigate examples from public opinion research, policy analysis, political science, economics, and sociology by using SAS "RM" and SPSS "RM" program syntax and data sets available at a free Web site. For those interested in understanding the past or predicting future possibilities and probabilities, this book reveals the power of time series analysis.