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Model a Wide Range of Count Time Series Handbook of Discrete-Valued
Time Series presents state-of-the-art methods for modeling time
series of counts and incorporates frequentist and Bayesian
approaches for discrete-valued spatio-temporal data and
multivariate data. While the book focuses on time series of counts,
some of the techniques discussed can be applied to other types of
discrete-valued time series, such as binary-valued or categorical
time series. Explore a Balanced Treatment of Frequentist and
Bayesian Perspectives Accessible to graduate-level students who
have taken an elementary class in statistical time series analysis,
the book begins with the history and current methods for modeling
and analyzing univariate count series. It next discusses
diagnostics and applications before proceeding to binary and
categorical time series. The book then provides a guide to modern
methods for discrete-valued spatio-temporal data, illustrating how
far modern applications have evolved from their roots. The book
ends with a focus on multivariate and long-memory count series. Get
Guidance from Masters in the Field Written by a cohesive group of
distinguished contributors, this handbook provides a unified
account of the diverse techniques available for observation- and
parameter-driven models. It covers likelihood and approximate
likelihood methods, estimating equations, simulation methods, and a
Bayesian approach for model fitting.
Model a Wide Range of Count Time Series Handbook of Discrete-Valued
Time Series presents state-of-the-art methods for modeling time
series of counts and incorporates frequentist and Bayesian
approaches for discrete-valued spatio-temporal data and
multivariate data. While the book focuses on time series of counts,
some of the techniques discussed can be applied to other types of
discrete-valued time series, such as binary-valued or categorical
time series. Explore a Balanced Treatment of Frequentist and
Bayesian Perspectives Accessible to graduate-level students who
have taken an elementary class in statistical time series analysis,
the book begins with the history and current methods for modeling
and analyzing univariate count series. It next discusses
diagnostics and applications before proceeding to binary and
categorical time series. The book then provides a guide to modern
methods for discrete-valued spatio-temporal data, illustrating how
far modern applications have evolved from their roots. The book
ends with a focus on multivariate and long-memory count series. Get
Guidance from Masters in the Field Written by a cohesive group of
distinguished contributors, this handbook provides a unified
account of the diverse techniques available for observation- and
parameter-driven models. It covers likelihood and approximate
likelihood methods, estimating equations, simulation methods, and a
Bayesian approach for model fitting.
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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