Bell / Holan / McElroy Economic Time Series

Modeling and Seasonality
Erscheinungsjahr 2018
ISBN: 978-1-4398-4658-2
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 554 Seiten

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.

Zielgruppe

Researchers and graduate students in statistics and econometrics.

Autoren/Hrsg.

Bell, William R.

Holan, Scott H.

McElroy, Tucker S.

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik

Inhaltsverzeichnis

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

Über Autor(innen)

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.