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Diagnostic Methods in Time Series (Paperback, 1st ed. 2021)
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Diagnostic Methods in Time Series (Paperback, 1st ed. 2021)
Series: SpringerBriefs in Statistics
Expected to ship within 12 - 17 working days
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This book contains new aspects of model diagnostics in time series
analysis, including variable selection problems and higher-order
asymptotics of tests. This is the first book to cover systematic
approaches and widely applicable results for nonstandard models
including infinite variance processes. The book begins by
introducing a unified view of a portmanteau-type test based on a
likelihood ratio test, useful to test general parametric hypotheses
inherent in statistical models. The conditions for the limit
distribution of portmanteau-type tests to be asymptotically pivotal
are given under general settings, and very clear implications for
the relationships between the parameter of interest and the
nuisance parameter are elucidated in terms of Fisher-information
matrices. A robust testing procedure against heavy-tailed time
series models is also constructed in the context of variable
selection problems. The setting is very reasonable in the context
of financial data analysis and econometrics, and the result is
applicable to causality tests of heavy-tailed time series models.
In the last two sections, Bartlett-type adjustments for a class of
test statistics are discussed when the parameter of interest is on
the boundary of the parameter space. A nonlinear adjustment
procedure is proposed for a broad range of test statistics
including the likelihood ratio, Wald and score statistics.
General
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