Economic Time Series (inbunden)
Inbunden (Hardback)
Antal sidor
Whittles Publishing
black and white 146 Illustrations 472 Equations 89 Tables black and white
472 Equations; 89 Tables, black and white; 146 Illustrations, black and white
247 x 165 x 31 mm
907 g
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Economic Time Series (inbunden)

Economic Time Series

Modeling and Seasonality

Inbunden Engelska, 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.
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"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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Övrig information

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.


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 Agustin Maravall and Domingo Perez 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 Pena 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