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Books > Business & Economics > Economics > Econometrics > Economic statistics
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Empirical Likelihood and Quantile Methods for Time Series - Efficiency, Robustness, Optimality, and Prediction (Paperback, 1st ed. 2018)
Loot Price: R1,521
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Empirical Likelihood and Quantile Methods for Time Series - Efficiency, Robustness, Optimality, and Prediction (Paperback, 1st ed. 2018)
Series: JSS Research Series in Statistics
Expected to ship within 18 - 22 working days
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This book integrates the fundamentals of asymptotic theory of
statistical inference for time series under nonstandard settings,
e.g., infinite variance processes, not only from the point of view
of efficiency but also from that of robustness and optimality by
minimizing prediction error. This is the first book to consider the
generalized empirical likelihood applied to time series models in
frequency domain and also the estimation motivated by minimizing
quantile prediction error without assumption of true model. It
provides the reader with a new horizon for understanding the
prediction problem that occurs in time series modeling and a
contemporary approach of hypothesis testing by the generalized
empirical likelihood method. Nonparametric aspects of the methods
proposed in this book also satisfactorily address economic and
financial problems without imposing redundantly strong restrictions
on the model, which has been true until now. Dealing with infinite
variance processes makes analysis of economic and financial data
more accurate under the existing results from the demonstrative
research. The scope of applications, however, is expected to apply
to much broader academic fields. The methods are also sufficiently
flexible in that they represent an advanced and unified development
of prediction form including multiple-point extrapolation,
interpolation, and other incomplete past forecastings.
Consequently, they lead readers to a good combination of efficient
and robust estimate and test, and discriminate pivotal quantities
contained in realistic time series models.
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