Economic Time Series

Economic Time Series: Modeling and Seasonality

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Features

    • Presents contributions from some of the leading researchers in the field from both academia and government statistical agencies
    • Includes new methodological developments and innovative empirical analyses
    • Focuses on applied problems of seasonal economic time series, such as periodic behavior, consequences of model misspecification, sampling error in time series data, diagnostics for seasonal adjustment, and outliers

    Summary

    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.

    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 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

    Editor Bio(s)

    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.

    Editorial Reviews

    "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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