An Introduction to Time Series Analysis and Forecasting : With Applications of SAS(R) and SPSS(R)

Monnie McGee

ISBN 10: 0127678700 / ISBN 13: 9780127678702

Published by Elsevier Inc, 2000

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9780127678702

Bibliographic Details

• Title: An Introduction to Time Series Analysis and Forecasting : With Applications of SAS(R) and SPSS(R)
• Publisher: Elsevier Inc
• Publication Date: 2000
• Language: English
• ISBN 10: 0127678700
• ISBN 13: 9780127678702
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 158H x 30D millimeters 235 millimeters width by 158 millimeters height by 30 millimeters depth
• Weight: 857 grams

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

About the Authors

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 Electricité de France.