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.
@introbul:Key Features @bul:* 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
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.
Introduction and Overview: Purpose. Time Series. Missing Data. Sample Size. Representativeness. Scope of Application. Stochastic and Deterministic Processes. Stationarity. Methodological Approaches. Importance. Notation.
Extrapolative and Decomposition Models: Introduction. Goodness-of-Fit Indicators. Average Techniques. Exponential Smoothing. Decomposition Methods. New Features of Census X-12.
Introduction of Box-Jenkins Time Series Analysis: Introduction. The importance of Time Series Analysis Modeling. Limitations. Assumptions. Time Series. Tests for Nonstationarity. Stabilizing the Variance. Structural or Regime Stability. Strict Stationarity. Implications of Stationarity.
The Basic ARIMA Model: Introduction to ARIMA. Graphical Analysis of Time Series Data. Basic Formulation of the Autoregressive Integrated Moving Average Model. The Sample Autocorrelation Function. The Standard Error of the ACF. The Bounds of Stationarity and Invertibility. The Sample Partial Autocorrelation Function. Bounds of Stationarity and Invertibility Reviewed. Other Sample Autocorrelation Funcations. Tentative Identification of Characteristic Patterns of Integrated, Autoregressive, Moving Average, and ARMA Processes.
Seasonal ARIMA Models: Cyclicity. Seasonal Nonstationarity. Seasonal Differencing. Multiplica
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- © Academic Press 2000
- 27th April 2000
- Academic Press
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@qu:"Robert Yaffee has performed an invaluable service to students of time series analysis by preparing an introduction to methods for analyzing time series data that includes examples drawn from the social sciences, and demonstrates how to program the procedures in SPSS and SAS. Introduction to Time Series Analysis and Forecasting will be a standard reference for years to come." @source:--DAVID F. GREENBERG, New York University, New York