Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time series modeling and seasonal adjustment, as is reflected both in the contents of the chapters and in their authorship, with contributors coming from academia and government statistical agencies.
For easier perusal and absorption, the contents have been grouped into seven topical sections:
By presenting new methodological developments as well as pertinent empirical analyses and reviews of established methods, the book provides much that is stimulating and practically useful for the serious researcher and analyst of economic time series.
Periodic Modeling of Economic Time Series
A Multivariate Periodic Unobserved Components Time Series Analysis for Sectoral U.S. Employment
Siem Jan Koopman, Marius Ooms, and Irma Hindrayanto
Seasonal Heteroskedasticity in Time Series Data: Modeling, Estimation, and Testing
Thomas M. Trimbur and William R. Bell
Choosing Seasonal Autocovariance Structures: PARMA or SARMA?
Estimating Time Series Components with Misspecified Models
Specification and Misspecification of Unobserved Components Models
Davide Delle Monache and Andrew Harvey
The Error in Business Cycle Estimates Obtained From Seasonally Adjusted Data
Tucker S. McElroy and Scott H. Holan
Frequency Domain Analysis of Seasonal Adjustment Filters Applied To Periodic Labor Force Survey Series
Richard B. Tiller
Quantifying Error in X-11 Seasonal Adjustments
Comparing Mean Squared Errors of X-12-ARIMA and Canonical ARIMA Model-Based Seasonal Adjustments
William R. Bell, Yea-Jane Chu, and George C. Tiao
Estimating Variance in X-11 Seasonal Adjustment
Stuart Scott, Danny Pfeffermann, and Michail Sverchkov
Practical Problems in Seasonal Adjustment
Asymmetric Filters for Trend-Cycle Estimation
Estela Bee Dagum and Alessandra Luati
Restoring Accounting Constraints in Time Series: Methods and Software for a Statistical Agency
Benoit Quenneville and Susie Fortier
Theoretical and Real Trading-Day Frequencies
Applying and Interpreting Model-Based Seasonal Adjustment: The Euro-Area Industrial Production Series
Agustín Maravall and Domingo Pérez
Outlier Detection and Modeling Time Series with Extreme Values
Additive Outlier Detection in Seasonal ARIMA Models by a Modified Bayesian Information Criterion
Pedro Galeano and Daniel Peña
Outliers in GARCH Processes
Luiz K. Hotta and Ruey S. Tsay
Constructing a Credit Default Swap Index and Detecting the Impact of the Financial Crisis
Yoko Tanokura, Hiroshi Tsuda, Seisho Sato, and Genshiro Kitagawa
Alternative Models for Seasonal and Other Time Series Components
Normally Distributed Seasonal Unit Root Tests
David A. Dickey
Bayesian Seasonal Adjustment of Long-Memory Time Series
Scott H. Holan and Tucker S. McElroy
Bayesian Stochastic Model Specification Search for Seasonal and Calendar Effects
Tommaso Proietti and Stefano Grassi
Modeling and Estimation for Nonseasonal Economic Time Series
Nonparametric Estimation of the Innovation Variance and Judging the Fit of ARMA Models
Priya Kohli and Mohsen Pourahmadi
Functional Model Selection for Sparse Binary Time Series with Multiple Inputs
Catherine Y. Tu, Dong Song, F. Jay Breidt, Theodore W. Berger, and Haonan Wang
Models for High Lead Time Prediction
Granville Tunnicliffe-Wilson and John Haywood
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.
"This book is an excellent collection of articles about the modeling and seasonal adjustments of economic time series data by the leading experts in this field. … As someone who often applies time series techniques to economic time series data in research, I found that I could still learn greatly by reading through this book. In particular, some of the discussions about the interactions of time series modeling and seasonal adjustments are very enlightening and useful. …Overall this volume contains a collection of articles that will prove to be quite useful to researchers who want to do serious applied work in modeling the economic time series data."
—Jun Ma, Journal of the American Statistical Association, March 2014
"The list of authors includes some of the leading contributors to the literature, including [editor] Bell. … All chapters contain both theoretical development and also empirical applications to economic series. … This volume is an ideal reference for those interested in recent developments in this literature."
—Alastair R. Hall, Journal of Times Series Analysis, June 2012
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