An Introduction to Time Series Analysis and Forecasting: With Application of Sas and Spss

Yaffee, Robert A./ McGee, Monnie

ISBN 10: 0127678700 / ISBN 13: 9780127678702

Published by Academic Pr, 2000

About this Item

1st edition. 528 pages. 9.50x6.50x1.25 inches. In Stock. Seller Inventory # __0127678700

Bibliographic Details

Title: An Introduction to Time Series Analysis and ...

Publisher: Academic Pr

Publication Date: 2000

Binding: Hardcover

Condition: Brand New

About this title

Synopsis:

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. 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.

• Describes principal approaches to time series analysis and forecasting
• Presents examples from public opinion research, policy analysis, political science, economics, and sociology
• Math level pitched to general social science usage
• Glossary makes the material accessible for readers at all levels

From the Back Cover:

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 methods of combining forecasts, model and forecast evaluation, along with a sample size analysis for common time series models. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical package makes it easy for the user to properly apply these techniques.