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150324s1999 xx |||||o 00| ||eng c |
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|a 10.2307/1392477
|2 doi
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|a (DE-627)JST045334293
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|a (JST)1392477
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Van Dijk, Dick
|e verfasserin
|4 aut
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|a Testing for Smooth Transition Nonlinearity in the Presence of Outliers
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|c 1999
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|a Text
|b txt
|2 rdacontent
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|a Computermedien
|b c
|2 rdamedia
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|a Online-Ressource
|b cr
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|a Regime-switching models, like the smooth transition autoregressive [STAR] model, are typically applied to time series of moderate length. Hence, the nonlinear features that these models intend to describe may be reflected in only a few observations. Conversely, neglected outliers in a linear time series of moderate length may incorrectly suggest STAR (or other) type(s of) nonlinearity. In this article we propose outlier robust tests for STAR-type nonlinearity. These tests are designed such that they have a better level and power behavior than standard nonrobust tests in situations with outliers. We formally derive local and global robustness properties of the new tests. Extensive Monte Carlo simulations show the practical usefulness of the robust tests. An application to several quarterly industrial production indexes illustrates that apparent nonlinearity in time series sometimes seems due to only a few outliers.
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|a Copyright 1999 American Statistical Association
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|a Asymptotic theory
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|a Influence function
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|a Lagrange multiplier-type tests
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|a Monte Carlo simulation
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|a Robust estimation
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Inferential statistics
|x Statistical estimation
|x Estimation methods
|x Estimators
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Descriptive statistics
|x Statistical distributions
|x Outliers
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|a Philosophy
|x Logic
|x Logical topics
|x Formal logic
|x Mathematical logic
|x Denotational semantics
|x Mathematical linearity
|x Nonlinearity
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|a Information science
|x Data products
|x Datasets
|x Time series
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Statistical models
|x Time series models
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|a Economics
|x Economic disciplines
|x Applied economics
|x Economic modeling
|x Economic models
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|a Mathematics
|x Applied mathematics
|x Statistics
|x P values
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4 |
|a Philosophy
|x Applied philosophy
|x Philosophy of science
|x Scientific method
|x Hypothesis testing
|x Null hypothesis
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Statistical theories
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|a Social sciences
|x Development studies
|x Development economics
|x Economic development
|x Economic transitions
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|a research-article
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|a Franses, Philip Hans
|e verfasserin
|4 aut
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|a Lucas, André
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of Business & Economic Statistics
|d American Statistical Association, 1983
|g 17(1999), 2, Seite 217-235
|w (DE-627)327084073
|w (DE-600)2043744-4
|x 07350015
|7 nnns
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|g volume:17
|g year:1999
|g number:2
|g pages:217-235
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|u https://www.jstor.org/stable/1392477
|3 Volltext
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|u https://doi.org/10.2307/1392477
|3 Volltext
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|a AR
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|d 17
|j 1999
|e 2
|h 217-235
|