An Introduction to Time Series Analysis and Forecasting: with Applications of SAS and SPSS

From Book News, Inc.
This advanced textbook examines the principal approaches to the analysis of time series processes and their forecasting. Yaffee (New York University) covers moving average, exponential smoothing, decomposition, ARIMA, intervention, transfer function, regression, error correction, and autoregressive error models. No exercises, but a glossary is provided.Copyright © 2004 Book News, Inc., Portland, OR


David F. Greenberg, New York University, New York
"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."


Review
From the prepublication reviews
"Robert Yaffee has performed an invaluable service to students of time series analysis by preparing an introduction to methods for analysing 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."
-DAVID F. GREENBERG, New York University, New York


Review
From the prepublication reviews
"Robert Yaffee has performed an invaluable service to students of time series analysis by preparing an introduction to methods for analysing 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."
-DAVID F. GREENBERG, New York University, New York


Book Description
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


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. 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 package makes it easy for the user to properly apply these techniques.


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This advanced textbook examines the principal approaches to the analysis of time series processes and their forecasting. Yaffee (New York University) covers moving average, exponential smoothing, decomposition, ARIMA, intervention, transfer function, regression, error correction, and autoregressive error models. No exercises, but a glossary is provided. Annotation c. Book News, Inc., Portland, OR (booknews.com)


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