Economic Time Series: Modeling and Seasonality, 1st Edition (Hardback) book cover

Economic Time Series

Modeling and Seasonality, 1st Edition

Edited by William R. Bell, Scott H. Holan, Tucker S. McElroy

Chapman and Hall/CRC

554 pages | 146 B/W Illus.

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Hardback: 9781439846575
pub: 2012-03-19
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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:

  • Section I deals with periodic modeling of time series, introducing, applying, and comparing various seasonally periodic models
  • Section II examines the estimation of time series components when models for series are misspecified in some sense, and the broader implications this has for seasonal adjustment and business cycle estimation
  • Section III examines the quantification of error in X-11 seasonal adjustments, with comparisons to error in model-based seasonal adjustments
  • Section IV discusses some practical problems that arise in seasonal adjustment: developing asymmetric trend-cycle filters, dealing with both temporal and contemporaneous benchmark constraints, detecting trading-day effects in monthly and quarterly time series, and using diagnostics in conjunction with model-based seasonal adjustment
  • Section V explores outlier detection and the modeling of time series containing extreme values, developing new procedures and extending previous work
  • Section VI examines some alternative models and inference procedures for analysis of seasonal economic time series
  • Section VII deals with aspects of modeling, estimation, and forecasting for nonseasonal economic time series

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.


"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

Table of Contents

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?

Robert Lund

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

Dominique Ladiray

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 MultipleInputs

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

About the Editors

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.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General