Economic Time Series：Modeling and Seasonality

Statisticians from academia and government agencies present methodological studies that illustrate the cross-fertilization between time series modeling and seasonal adjustment. They cover the periodic modeling of economic time series, estimating time series components with misspecific models, quantifying error in X-11 seasonal adjustments, practical problems in seasonal adjustment, outlier detection and modeling time series with extreme values, alternative models for seasonal and other time series components, and modeling and estimation for non-seasonal economic time series. Annotation Ac2012 Book News, Inc., Portland, OR (booknews.com)

William R. Bell, Ph.D., is the Senior Mathematical Statistician for Small Area Estimation at the U.S. Census Bureau. He is a recognized researcher in the area of modeling and adjustment of seasonal economic time series. He has also worked on development of related computer software, including software for RegARIMA modeling of seasonal economic time series (for the X-12-ARIMA seasonal adjustment program), and the REGCMPNT program for time series models with regression effects and ARIMA component errors.

Scott H. Holan, Ph.D., is an Associate Professor of Statistics at the University of Missouri. He is the author of over 30 articles on topics of time series, spatio-temporal methodology, Bayesian methods and hierarchical models. His work is largely motivated by problems in federal statistics, econometrics, ecology and environmental science.

Tucker S. McElroy, Ph.D., is a Principal Researcher for Time Series Analysis at the U.S. Census Bureau. His research is focused primarily upon developing novel methodology for time series problems, such as model selection and signal extraction. He has contributed to the model diagnostic and seasonal adjustment routines in the X-12-ARIMA seasonal adjustment program, and has taught seasonal adjustment to both domestic and international students.