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An Introduction to Time Series Analysis and Forecasting: With Applications of SAS(R) and SPSS(R) - Tapa dura
Sinopsis
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.
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Acerca de los autores
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.
De la contraportada
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.|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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- EditorialAcademic Press
- Año de publicación2000
- ISBN 10 0127678700
- ISBN 13 9780127678702
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas556
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