Economic Time Series: Modeling and Seasonality

544 Pages·2012·6.206 MB·English

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

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
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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
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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
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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
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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
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