Economic Time Series: Modeling and Seasonality PDF
Preview Economic Time Series: Modeling and Seasonality
Economic Time Series Modeling and Seasonality K12089 Chapter: 0 page: i date: February 14, 2012 TThhiiss ppaaggee iinntteennttiioonnaallllyy lleefftt bbllaannkk Economic Time Series Modeling and Seasonality Edited by William R. Bell Scott H. Holan Tucker S. McElroy K12089 Chapter: 0 page: iii date: February 14, 2012 CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2012 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20111007 International Standard Book Number-13: 978-1-4398-4658-2 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmit- ted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright. com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Contents Preface ix Editors xiii Contributors xv I Periodic Modeling of Economic Time Series 1 1 A Multivariate Periodic Unobserved Components Time Series Analysis for Sectoral U.S. Employment 3 Siem Jan Koopman, Marius Ooms, and Irma Hindrayanto 2 Seasonal Heteroskedasticity in Time Series Data: Modeling, Estimation, and Testing 37 Thomas M. Trimbur and William R. Bell 3 Choosing Seasonal Autocovariance Structures: PARMA or SARMA? 63 Robert Lund II Estimating Time Series Components with Misspecified Models 81 4 Specification and Misspecification of Unobserved Components Models 83 Davide Delle Monache and Andrew Harvey 5 Error in Business Cycle Estimates Obtained from Seasonally Adjusted Data 109 Tucker S. McElroy and Scott H. Holan 6 Frequency Domain Analysis of Seasonal Adjustment Filters Applied to Periodic Labor Force Survey Series 135 Richard B. Tiller v K12089 Chapter: 0 page: v date: February 14, 2012 vi Contents III Quantifying Error in X-11 Seasonal Adjustments 159 7 Comparing Mean Squared Errors of X-12-ARIMA and Canonical ARIMA Model-Based Seasonal Adjustments 161 William R. Bell, Yea-Jane Chu, and George C. Tiao 8 Estimating Variance in X-11 Seasonal Adjustment 185 Stuart Scott, Danny Pfeffermann, and Michail Sverchkov IV Practical Problems in Seasonal Adjustment 211 9 Asymmetric Filters for Trend-Cycle Estimation 213 Estela Bee Dagum and Alessandra Luati 10 Restoring Accounting Constraints in Time Series— Methods and Software for a Statistical Agency 231 Benoˆıt Quenneville and Susie Fortier 11 Theoretical and Real Trading-Day Frequencies 255 Dominique Ladiray 12 Applying and Interpreting Model-Based Seasonal Adjustment—The Euro-Area Industrial Production Series 281 Agust´ın Maravall and Domingo P´erez V Outlier Detection and Modeling Time Series with Extreme Values 315 13 Additive Outlier Detection in Seasonal ARIMA Models by a Modified Bayesian Information Criterion 317 Pedro Galeano and Daniel Pen˜a 14 Outliers in GARCH Processes 337 Luiz K. Hotta and Ruey S. Tsay 15 Constructing a Credit Default Swap Index and Detecting the Impact of the Financial Crisis 359 Yoko Tanokura, Hiroshi Tsuda, Seisho Sato, and Genshiro Kitagawa VI Alternative Models for Seasonal and Other Time Series Components 381 16 Normally Distributed Seasonal Unit Root Tests 383 David A. Dickey K12089 Chapter: 0 page: vi date: February 14, 2012 Contents vii 17 Bayesian Seasonal Adjustment of Long Memory Time Series 403 Scott H. Holan and Tucker S. McElroy 18 Bayesian Stochastic Model Specification Search for Seasonal and Calendar Effects 431 Tommaso Proietti and Stefano Grassi VII Modeling and Estimation for Nonseasonal Economic Time Series 457 19 Nonparametric Estimation of the Innovation Variance and Judging the Fit of ARMA Models 459 P. Kohli and M. Pourahmadi 20 Functional Model Selection for Sparse Binary Time Series with Multiple Inputs 477 Catherine Y. Tu, Dong Song, F. Jay Breidt, Theodore W. Berger, and Haonan Wang 21 Models for High Lead Time Prediction 499 G. Tunnicliffe Wilson and John Haywood Index 525 K12089 Chapter: 0 page: vii date: February 14, 2012 TThhiiss ppaaggee iinntteennttiioonnaallllyy lleefftt bbllaannkk Preface Amajortopicofmethodologicalresearchintimeseriesanalysisandeconomet- ricsoverthepastfivedecadeshasbeenthemodelingandseasonaladjustment of economic time series. The reason for this is simple: tens of thousands of economic time series are seasonally adjusted and published every month or quarterbydozensofstatisticalagenciesaroundtheworld,andthesedatapro- vide much of the empirical foundation for macroeconomic policymaking. The sheer bulk of these data warrants continual improvements to methodology. Research on time series modeling, and particularly on seasonal time series modeling, exploded with the publication of the book, Time Series Analysis: Forecasting and Control, by Box and Jenkins in 1970. Also of considerable importance was the not unrelated appearance around that time of computer software for fitting seasonal time series models. First, computer programs written for this specific purpose appeared, and then time series modeling ca- pabilities began to appear in various statistical software packages, making these modeling capabilities widely available. Parallel developments in seasonal adjustment methodology occurred sep- arately and even slightly earlier. In 1953, Julius Shiskin introduced the first computerized method of seasonal adjustment on the U.S. Census Bureau’s UNIVAC I computer. This approach, called Method I, was quickly replaced by Method II, X-1 variant, with successive improvements culminating in the mid-1960swiththefamousX-11program.Furtherdevelopmentssawseasonal adjustment start borrowing recently developed techniques from time series modeling, as in the X-11-ARIMA method and program of Estela Dagum and StatisticsCanada,andinthedevelopmentoftimeseriesmodel-basedmethods of seasonal adjustment. Additionally, problems faced in seasonal adjustment inspired further developments in seasonal time series modeling methodology. Tofacilitatethiscross-fertilization,in1976and1981theCensusBureauorga- nized conferences on the modeling and seasonal adjustment of economic time series. These conferences brought academic statisticians and econometricians doingresearchontimeseriesmodelingtogetherwithgovernmentstatisticians working on the enhancement of seasonal adjustment methods. In the midst of much of this exciting activity was David Findley, who ar- rived at the U.S. Census Bureau in the early 1980s to supervise a small staff focused on research and computer software development to improve the tech- niquesofmodelingandseasonaladjustmentofeconomictimeseries.Inhisown research, David made important contributions to time series model selection ix K12089 Chapter: 0 page: ix date: February 14, 2012
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