By
Robert Yaffee, New York University, New York, U.S.A.
Monnie McGee, Hunter College, City University of New York
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.
Audience:
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.
